Research ArticleAnalysis of Wind Data Calculation of Energy Yield Potential andMicrositing Application with WAsP

Fatih Topaloglu and Huseyin Pehlivan

Department of Computer Engineering Faculty of Engineering Karadeniz Technical University 61080 Trabzon Turkey

Correspondence should be addressed to Fatih Topaloglu f_topaloglu023hotmailcom

Received 7 February 2018 Revised 5 April 2018 Accepted 24 April 2018 Published 30 May 2018

Academic Editor Ismail Gultepe

Copyright copy 2018 Fatih Topaloglu and Huseyin Pehlivan is is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in anymedium provided the original work isproperly cited

e parameters required for building a wind power plant have been calculated using the fuzzy logic method by means of WindAtlas Analysis and Application Program (WAsP) in this study Overall objectives of the program include analysis of raw dataevaluation of wind and climate construction of a wind atlas and estimation of wind power potential With the analysis performedin the application the average wind velocity average power density energy potential from micrositing capacity factor unit costprice and period of redemption have been calculated which are needed by the project developer during the decision-making stageand intended to be used as the input unit in the fuzzy logic-based system designed It is aimed at processing the parameterscalculated by the designed fuzzy logic-based decision-making system at the rule base and generating a compatibility factor that willallow for making the final decision in building wind power plants

1 Introduction

Various methods are being used for the determination ofwind power potentials One of the most important of these isWAsP (Wind Atlas Analysis and Application Program)which is made in the Denmark Riso National Laboratory andused to generate the wind atlas of the European continent(EWA) [1]

WAsP is being used for the determination of regionalwind atlas statistics and energy yield potential by using thewind speed and direction information as well as the obstaclesaround the wind observation station land surface rough-ness and land topographical characteristics

Fuzzy logic is a multiple-logic system that is developedagainst the binary-logic system and assigns membershipgrades to variables used in everyday life and determines theproportions at which events occur Everything is right orwrong in two-valued logic In fuzzy logic everything in-cluding truth is only a matter of degree

Wind is formed by heating and cooling different surfacesat different speeds on the earth from the sunrise until sunsetKinetic energy of the air in motion is called as wind power

Since the humans are being concerned with the environ-ment the interest in utilisation of renewable power is in-creasing Wind power among the renewable energy is beingused widely [2] Due to the fact that Turkey is a country withthree sides surrounded by sea it is in a very importantposition in terms of wind power potential e calculatedwind power potential of Turkey is around 88000MW anda great majority of this potential is located in Aegean SouthMediterranean and Marmara regions [3]

In this study analysis of wind measurement data ob-tained at 10m height and 10-minute interval has beencarried out with WAsP and the wind speed average powerdensity form parameter scale parameter dominant winddirection and the Hellmann coefficient have been calculatedin accordance with theWeibull distribution While planningto install the wind turbines to the plant the required regionalwind atlas has been generated Meteorological data fromweather stations outside urban areas are sometimes usedwhere wind measurements are not available [4 5]

In this micrositing study a wind plant with 12MWin the region has been planned to be established and the useof a 2MW V80-type turbine belonging to the VESTAS

HindawiAdvances in MeteorologyVolume 2018 Article ID 2716868 10 pageshttpsdoiorg10115520182716868

company available in theWAsP library has been planned forthis establishment Annual energy generation values of theturbines have been calculated as a result of the micrositingstudy performed in this way

e capacity factor which is the most important pa-rameter during the definition of wind energy potential ofone region is identified as the proportion of energy gen-erated by a wind power plant to the energy that has to begenerated at nominal power [6] With the calculations madethe capacity factor of the region and the annual generationvalue of the plant in kWh and the amortization period havebeen found

2 WAsP Model

WAsP has been used during the analysis of wind data andenergy generation potential WAsP is a computer programdeveloped in the Denmark Riso National MeteorologyLaboratory which is performing analysis with the assump-tion that wind speed data comply with the Weibull distri-bution with 2 parameters

21 Basic Information Used by WAsP WAsP carries outsome analyses by assessing four various basic data in itssubmodels Hourly wind data roughness data of the regionobstacle data in the near vicinity and topographical data ofthe region are the basic information used by the programFor the wind farm location it is also necessary to enter datasuch as the power curve thrust coefficient curve turbine hubheight and turbine rotor diameter in the program [7]

22 General Purposes of WAsP It is possible to group themunder four main titles of general purpose of WAsP so as toanalyze the raw data generate the wind atlas and assess thewind climate and wind energy potential

WAsP is performing the time-series analysis by adjustingthe raw meteorological data including wind direction andspeed data Weibull parameters are being calculated with theperformed analysis [7] Wind speed histograms may beconverted into wind atlas series Histograms may be gen-erated by the data analysis method or may be directly madewith standard climatologic tables [7]

It is possible to carry out wind climate assessment in anyregion by using the wind atlas generated by WAsP or byusing the data series or data from other reliable sourcesWind climate is being assessed withWeibull parameters andregional distribution of the wind [7] WAsP also calculatesthe total energy to be obtained from wind Programmay alsoprovide annual average energy to be obtained from a windturbine and an energy curve of the subject turbine

3 Submodels Used at the Application

WAsP uses some submodels while determining the windpotential ese submodels are obstacle screening oro-graphic and roughness exchange models

31 Roughness Exchange Model e logarithmic windprofile is only applicable where the surface is homogeneousPressure changes as per the average surface tensions andsurface conditions of surface wind speed until where thegradient force is equal to the fraction force For theroughness length values belonging to two different surfacesthe following equation may be written for the increase of thelimit layer height values

h

z0ln

h

z0minus 11113888 1113889 constant

x

z0

z0 max z01 z02( 1113857

(1)

where h is the boundary layer height value x is the surfacecondition distance and z0 z01 and z02 are the roughnesscoefficients (constant 09)

e wind profile is deformed below the level h and theconstant value is 09 If we accept the neutral wind profile atheight h the difference in the surface friction speed may bemodelled empirically and this can be shown as follows

Ulowast2Ulowast1

ln hz01( 1113857

ln hz02( 1113857 (2)

where Ulowast2 is the friction speed at the considered point andUlowast1 is the surface friction speed

In this equation friction speed at the point taken intoconsideration is the surface friction speed

32 Shelter Model e wind profile is deformed at closedistances and at lower parts of the flow of wind coming fromthe turbine For this these objects creating an obstacle effectshould be separately studied Generally there should bea distance of two objectsrsquo height on the upper wind part ofthe obstacle and five objectsrsquo height on the lower wind part ofthe obstacle Since the meteorological stations are beingimpacted by the obstacles in the vicinity this model correctsthe fault caused by the obstacle on the data

e results of wind tunnel studies on simple two-dimensional objects have the following equation

Δu

u 98

za

h1113874 1113875

014x

h(1minusP)η exp minus067η15

1113872 1113873 (3)

where P is the open areatotal area h is the height of theobstacle za is the height of the anemometer and x is theunder wind region distance

In this equation η is expressed as follows

η za

h

032ln hz0( 1113857

x

h1113888 1113889

minus047

(4)

where za is the height of the anemometer h is the height ofthe obstacle x is the under wind region distance and z0 isthe roughness coefficient

If there are more than one object they have beenassessed with sectors formed with angles of 30deg

2 Advances in Meteorology

33OrographicModel In this model on themeasured winddata faults of local impacts caused by the topographicalstructure are being corrected [8] For the remedial of theseimpacts a horizontal scale of 20ndash30 km shall be taken intoaccount Primarily potential current deformations causedby the land topography shall be calculated e speedperturbation is

u ΔX (5)

where X is the potential and u is the three-dimensionalspeed deformation vector

For a given radius R for the potential ow at polarcoordinates the following equation shall be written

Xj KnjJn Cnjr

R( )exp( ln Φ)exp minusCnj

z

R( ) (6)

where Knj is the random coecient Jn n is the level Besselfunction r is the radiusΦ is the azimuth z is the height andCnj jnprime s i which is zero

For the specic problems coecients shall be calculatedfrom surface kinematical limit conditions

Jn Cnjr

R( ) (7)

where Jn n is the level Bessel function Cnj jnprime s i which iszero and r is the radius

Functions are as FourierndashBessel series Knj coecientsmay be dened independently e model forms the graydata on the contour lines on the topographical map Sen-sitivity of the model depends on the density of contours

4 Wind Atlas Analysis andProgram Application

In order to make the wind power more competitive againstother power generation methods bigger wind turbines are

being designed and established on the clusters especially onthe overseas regions called as the wind plant [9]

To be able to establish a wind power plant both me-teorological and nancial parameters are needed is alsoshows that one wind plant establishment actually requires tothink and use more than one discipline at the same time Forthis reason during the analysis WAsP has been used whichhas been already used for the preparation of the EuropeanWind Atlas and Turkish Wind Atlas

is study is aimed at nding out the average wind speedaverage power density energy yield potential obtained asa result of micrositing capacity factor amortization periodand unit cost price required for the establishment of the windpower plant as a result of the performed analysis ese ob-tained values are being planned to be used to generatea compliance factor for the establishment of the wind powerplant by scaling in the rule basis prepared in the fuzzy logicmethod In Figure 1 MatlabSimulink design of the inspectionsystem is seen which is planned to be used as an input unit ofobtained parameters

41 Geographical Status of Arapgir Province Selected for isStudy Mathematical location is between 39deg05prime N latitudeand 38deg30prime E longitude We can examine the province inthree parts in terms of surface features

(i) Mountainous areas in the western and northernparts of the province

(ii) e section east of the district center(iii) Medium-height and slightly rugged Arapgir rubble

forming the southern part [10 11]

As a result of continental climate most part of the areasof the province are coated with steppeis means that forest

Scope 2

Display 2

Display 1

Scope 1

Scope

Display

Amortizationperiod

kWhcost

Unpredictableexpense

Capacityfactor

Averagepower density

Fuzzy logiccontroller

Fuzzy logiccontroller 1

Fuzzy logiccontroller 2

Averagewind speed

0

0

0

0

0

0

Figure 1 MatlabSimulink model of the designed system

Advances in Meteorology 3

land is very small Existing forest lands are not in goodqualityese are mostly very small oak forests Satellite viewtaken from Google Earth of Arapgir Province is shown inFigure 2 In this study topographical obstacles have beentaken into consideration [10 11]

42 Assessment of Wind Measurement Data In this studywind speed and wind direction data belonging to 10m of theheight taken from OMGI (Automatic Meteorological Ob-servation Station) are being used between January 2014 andDecember 2015

Monthly average of the measurements taken at 10m ofthe height in the region is shown in Table 1 When the tableis examined the highest average wind speed is obtained as

41ms in July and the lowest average wind speed is obtainedas 16ms in January

In Table 2 average wind severity and average powerdensity values belonging to the region obtained as a result ofthe analysis made in WAsP are being shown

Twelve sectors have been used in Figure 3 as a result ofthe calculations made in WAsP and the dominant winddirection has been observed as SE at 120deg sector

Most frequently the distribution used for the calculationof wind power potential is the Weibull distribution isdistribution has been found by the Swedish physicistWaloddi Weibull is distribution is considerably exibleand simple and also complies with the real data In otherwords since the Weibull distribution is in compliance withwind speed data it is generally accepted in wind poweranalysis [12] e Weibull distribution function is as follows[13]

hw(V) k

cXV

c( )

kminus1Xe minus(Vc)

k[ ] (8)

where k is the gure parameter (parameter showing the windspeed distribution form) c is the scale parameter (relativecumulative frequency for wind speed) and hw(V) is thepossibility density function of wind speed

In order to nd Weibull parameters (k and c) wind dataof the land are required Analytical and experimentalequations used to nd Weibull parameters are written asfollows [14]

k auU( )minus1086

c UX 0568 +0433k

( )minus1k

(9)

where au is the standard deviation and U is the averagespeed

Arapgir

Figure 2 Satellite view of Arapgir Province

Table 1 Monthly average wind speed measured at 10m height

Month Average wind speed at10m height (ms)

January 16February 2March 26April 28May 31June 38July 41August 36September 29October 19November 25December 22

Table 2 Average wind severity and average power density of theregion

Calculated valuesAverage wind severity (ms) 275Average power density (Wm2) 53

Number of spindles

0

5000

10000

15000

20000NNE

NE

ENE

ESE

SE

SSESSW

SW

WSW

WNW

NW

NNW

Figure 3 Wind rose belonging to Arapgir Province

4 Advances in Meteorology

After examining the wind data in detail and performingthe above-given calculations according to the Weibulldistribution in Figure 4 the average wind speed is found as275ms the average power density is found as 53Wm2 thegure parameter is found as 151 and the scale parameter isfound as 42ms

Wind speed measurements are generally being made atdicenterent heights than at the tower heights of wind turbinesFor this reason these measured wind speeds are being ex-trapolated to turbine tower heights by using the formulaknown as 17 wind power law [14] As known by using theHellmann coecient estimated wind speed values ata requested height may be calculated from the wind speedvalues measured at a specic height Wind speed datameasured at a specic height may be transferred to otherheights by using the following equation

U Urrefh

href( )

μ

(10)

whereU is the wind speed at the height to be calculatedUrrefis the wind speed at the height where the measurementresults are known h is the height of the point to be calculatedfrom the surface href is the height of the point where themeasurement results are known from the surface and μ isthe Hellmann coecient

In this study wind speed values obtained at 10m inArapgir with ten minutes of interval have been moved to80m which is the turbine hub height with the calculatedHellmann coecient

e Hellmann coecient can be taken as 17 in the mostgeneral case However the other ways can be used to de-termine it more accurately [15]

Calculation in terms of roughness length

μ 0096log10z0 + 0016 log10z0( )2 + 024 (11)

Calculation in terms of speed and height

μ 037minus 0088ln Urref( )1minus 0088ln Uref 10( )

(12)

Calculation of roughness length and speed

μ k0 1minus 055log Uref( )[ ] (13)

Equation (13) is proposed by the NASA for the Hell-mann coecient By using the existing data the change ofthe Hellmann coecient as per the direction has been ex-amined in addition to the analysis made according to thewind ow directione average of the Hellmann coecienthas been calculated as 021 with the analysis of 61293 data intotal for all sectors As shown in Figure 5 the highestHellmann coecient has been found in SE which is thedominant wind direction

43 Generation of the Wind Atlas In this study wind atlasstatistics have been prepared belonging to the plant area withthe help of WAsP software by using the frequency distri-bution table obtained from wind speed and direction databelonging to 10 m of the height obstacles in near vicinityroughness data and digitized map with a scale of 125000representing the region topography

Data imported into WAsP are being analyzed and theaverage wind severity map of the region in Figure 6 andpower density map of the area in Figure 7 have been visuallyobtained e wind turbine-locating process shall be carriedout on the points where the wind severity is high by using theaverage wind speed calculated in the region

While planning the installation of wind turbines in thesite wind atlas is being used By using WAsP and wind atlasstatistics locations where the power generation amount willbe high on the digital map may be dened from colourdistribution

44 Micrositing and Turbine Selection In this study it hasbeen planned to establish a wind plant of 12MW in the areaand for this establishment it has been planned to use 2MWof the V80-type turbine belonging to the VESTAS companywithin the WAsP library In order to minimize the in-terference of turbines with each other the micrositing studyhas been performed

During this micrositing study design has been made soas to keep the track area losses of the turbines on each otherin the minimum level Furthermore while designing theoptimum turbine placement so as to obtain a maximumyield wind turbines have been placed on the dominantwind direction not only taking into consideration thegeneration amount but also the operability limits of the

c 42 msk 151U 275 msp 53 Wm2

0 5 10 15 200

25

()

Figure 4 e Weibull distribution belonging to Arapgir Province

00102030405

E S W NEN

EES

EN

EN

NE

NN

WN

W SE SSE

SSW SW

WN

WW

SW

Hellmann coefficient

Figure 5 Change of the Hellmann coecient as per winddirection

Advances in Meteorology 5

turbines in technical terms In Figure 8 the micrositingstudy has been shown where six 2MW VESTAS V80 windturbines are used

Following this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are

used electricity generation of annual gross 41316 GWhand net 38352 GWh has been calculated As a resultof the interference of turbines with each other it hasbeen understood that there will be a 717 loss Annualgeneration values have been shown in Table 3 in themicrositing study where 6 VESTAS V80 wind turbinesare used

In Table 4 generation and loss values for each turbine aregiven for the micrositing study where 6 V80 wind turbineshave been used

As can be shown in Table 4 when annual net powergeneration values are being considered the maximum gen-eration amount 7082GWh was at turbine number 4 and theminimum generation amount 5713GWh was at turbine 6During the micrositing study it has been calculated that themaximumwake loss 979was at turbine 6 and theminimumwake loss 534 was at turbine number 4

45 Capacity Factor of the Area e capacity factor whichis an important parameter and has to be known both bythe generators and consumers is the division of energygenerated in a specific time frame to the maximumenergy that can be generated at that specific time frame[16 17]

In this study the capacity factor has been calculated forthe turbine used during micrositing and turbine selectionstudies and generated micrositings

CF ET

TlowastPR (14)

where CF is the capacity factor ET is the generated totalpower PR is the nominal power value and T is the time

In the micrositing study the annual calculated genera-tion amount for six 2MW V80 wind turbines maximumamount that can be generated from turbines and capacityfactor are shown in Table 5

In case of operation under nominal power of six 2MWturbines during 8760 hours in one year they may generatemaximum 8760times12105120GWh power in one year As

Figure 7 Power density map of the area

Figure 8 Micrositing process of turbines

Table 3 Annual generation values of 6 V80 wind turbines

Parameter Total Min MaxNet annual generation (GWh) 38352 5713 7082Gross annual generation (GWh) 41316 6034 7897Wake (track) loss () 7173 mdash mdash

Figure 6 Average wind severity map calculated in the area

Table 4 Annual generation and loss values of 6 V80 wind turbines

Turbinenumber

Hubheight (m)

Net annualgeneration (GWh)

Wake(track) loss

Turbine 1 80 6789 709Turbine 2 80 6145 618Turbine 3 80 5824 962Turbine 4 80 7082 534Turbine 5 80 6799 829Turbine 6 80 5713 979

6 Advances in Meteorology

a result of the calculations made in WAsP it has beendetermined that the wind power generated by the windpower plant formed by six 2MW turbines shall generate38352GWh power in one year and the capacity factor shallbe realized as 38352105120 03648 in other words3648 e capacity factor which is calculated at and above25 for the wind turbines deemed appropriate for the in-vestment in Turkey [18]

46 Economical Analysis of the Data One of the most im-portant studies that have to be carried out while establishinga wind turbine to a region is the calculation of kWh powercost Generally the cost of one wind power project per kWhis found by proportioning the annual total cost to the annualpower generation amount

e annual power generation amount changesdepending on the parameters such as the hub height ofturbine rotor diameter average wind severity of the areaand annual cost may be correlated with turbine priceturbine foundations inner site road construction costsinvestment costs and project lifetime In Figure 9 the coststructure is shown per kWh for wind power projects [19]

In this study within the rst establishment turbine costnetwork connection foundation and establishment costselectricity installation and road construction and control

Table 5 Capacity factor of the area

Turbinemodel

Turbinenumber

Installedpower

Annual calculated production amount(GWh)

Nominal power to be produced(GWh)

Capacity factor()

VESTAS V80 6 12 38352 10512 3648

Turbineand

installation

Project lifeCapital cost

pa

Turbine price inturbine-based roads

Rotor diameterhub height Average wind speed

region properties

Annual capitalcosts

Total annualcosts

Energy costper kWh

Annual energyproduction

Annual operatingand

maintenance costs

kwh

kwh

Figure 9 Cost structure per kWh for wind power projects

Table 6 Turbine establishment cost [20ndash26]

Establishment parameters Cost (euro)Turbine and installation 9000000Project preparation costs (permission and licences) 650000Land rental cost 100000Network connections (cable transformer andcommunication) 1800000

Foundation and installation (foundation roadand site) 1200000

Finance (consultancy insurance and bank) 450000Total 13200000

Table 7 Annual income-expense table [20ndash26]

euroyearElectricity sales income 2109360Emission sales income 82608Annual operational income (euroyear) 2191168Rental amount 100000Maintenance-repair insurance 130000Labour 30000Annual licence cost 1000General administrative costs 5000Annual operational expenses (euroyear) 266000Annual operational margin (euroyear) 1925168

Advances in Meteorology 7

systems have been considered as mentioned in the report ofthe European Wind Power Union and land rental con-sultancy maintenance and repair and yearly licence costshave been included into the analysis as permanently paidcosts within years (Table 6)

Annual 38352GWh power generation has been calcu-lated for the wind power plant formed with six 2MWVESTAS V80 wind turbines In this case annual income hasbeen calculated as 2109360 euro with 55 eurocentkWh electricitysales price Emission sales income for the 38352GWh powergeneration has been calculated as 82608 euro annually Asa result of the calculations made annual income and ex-penses as well as the annual margin are shown in Table 7

In this study operational expenses maintenance repairinsurance rental and licence costs and some valueschanging as per year such as interest have been re ected asaverage values in the income-expense table by taking intoconsideration the project lifetime In case of selling theelectricity at 55 eurocentkWh at the wind power plant formedby 6 VESTAS V80 wind turbines it is seen that the cash owwill pass to the positive state at 7 years and the projectreturns at year 7

In this study cost per kWh for the capacity factor ob-tained from selected turbines has been calculated By usingthe capacity factor as 3648 calculated for the VESTAS V80turbine used in the established system the annual generationamount of the plant in kWh has been found

In order to calculate the unit cost of electricity powerobtained from wind turbines it is necessary to know theregaining factor of investment e regaining factor ofcapital (C) is calculated with the following formula

C i(1 + i)n

(1 + i)n minus 1 (15)

where i is the interest rate () and n is the turbine lifetime(years)

Generation cost is as follows

euroU CT(C + I)

E (16)

where euroU is the generation cost (eurokWh) I is the servicemaintenance and insurance (operational) expenses () CTis the total establishment cost of the wind turbine (euro) and Eis the annual generated power amount (kWh)

e power to be generated by the wind turbine(kWhyear) in the region (E) where the establishment of thewind turbine is planned is dened with the followingformula

E ηkay middot Cp middot12middot ρh middot S middotsum

ki1Δti middot U3

r (17)

where Δti is the time interval (h) U is the wind speed (ms)ρh is the air density (kgm

3) r is the radius S is the scannedarea by the wind turbine (m2) Cp is the power coecientηkay is the fraction losses at bearings (eg 0996) losses at thegear box (eg 0972) and losses at the electricity generator(eg 094) and general total coecient (may be consideredas approximately ηkay 09 at calculations)

C 0075(1 + 0075)25

(1 + 0075)25 minus 1 008971

U 13200000(008971 + 002)

33955200 00426 euroKWh

426 eurocKWh

(18)

In the calculations made unit cost values to be used asthe input unit in the fuzzy logic system are found as426 eurockWh

71

309

3648

2

426

7Amortization

period

kWhcost

Unpredictableexpense

Capacityfactor

Averagepower density

Averagewind speed

Fuzzy logiccontroller

Fuzzy logiccontroller 2

Display 1

Scope 1

Scope

Display

Display 2

Scope 2

Fuzzy logiccontroller 1

847256

835742

775063

Figure 10 Display of the result values generated by the fuzzy-based decision-making system designed according to the input valuescalculated by the WAsP

8 Advances in Meteorology

5 Conclusion

In this study average wind speed and wind direction data ofArapgir province measured at 10m height and 10-minuteintervals and taken from OMGI (Automatic MeteorologicalObservation Station) were analyzed by WAsP software Inthe analysis made the average wind speed is found as275ms the average power density is found as 53Wm2 thefigure parameter is found as 151 and the scale parameter isfound as 42ms obtained at 10m according to the Weibulldistribution

As a result of the calculations made in WAsP 12 sectorshave been used in the wind mill and dominant wind di-rection has been observed as SE at 120deg sector e change ofthe Hellmann coefficient as per the direction by usingexisting data in addition to the analysis made as per the windblowing has been examined and the average Hellmanncoefficient has been calculated as 034

As a result of this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are usedwith the wind power plant to be established in the region ithas been calculated that annual gross 41316GWh and net38352GWh electricity shall be generated It has been un-derstood that 717 loss shall occur as a result of the in-terference of wind turbines with each other

In case six 2MW turbines operate under nominal powerduring 8760 hours a maximum of 105120GWh energy maybe generated As a result of the calculations made in WAsPit has been determined that the wind power plant formed bysix 2MW turbines shall generate 38352GWh energy in oneyear and the capacity factor shall be realized as 3648

Annual generation values of the plant have been found as426 eurockWh by using 3648 capacity factor calculated forthe VESTAS V80 turbine used in the established systemeamortization period is calculated as 7 years Unpredictableexpenditure was selected as 2 of the initial installation coste height of the tower of the wind turbine was determinedas 80m e wind speed at 80m height was calculated as71ms e power density at the height of 80m was cal-culated as 309 In Figure 10 the final relevance factor ofrunning the system in the MatlabSimulink model is 8357

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors certify that there are no actual or potentialconflicts of interest in relation to this article

References

[1] October 2017 httpswwwmgmgovtrgenelruzgar-atlasiaspx[2] A Bechkaoui A Ameur S Bouras and A Hadjadj ldquoOpen-

circuit and inter-turn short-circuit detection in PMSG forwind turbine applications using fuzzy logicrdquo Energy Procediavol 74 pp 1323ndash1336 2015

[3] E T Karagol and I KavazDunyada ve Turkiyersquode YenilenebilirEnerji Turkuvaz Haberlesme ve Yayıncılık AS Vol 197Istanbul Turkey 2017

[4] E Kovac-Andriḉ T Radanoviḉ I Topaloviḉ B Markoviḉand N Sakaḉ ldquoTemporal variations in concentrations ofozone nitrogen dioxide and carbon monoxide at OsijekCroatiardquo Advances inMeteorology vol 2013 Article ID 4697867 pages 2013

[5] J L Pearce J Beringer N Nicholls R J Hyndman andN J Tapper ldquoQuantifying the influence of local meteorologyon air quality using generalized additive modelsrdquoAtmosphericEnvironment vol 45 no 6 pp 1328ndash1336 2011

[6] S Mathew Wind Energy Fundamentals Resource Analysisand Economics Springer Berlin Germany 2006

[7] S Mentes Ruzgar Enerjisi ve Sistemleri Ders Notları ITUUccedilak Uzay Fakultesi Istanbul Turkey 2008

[8] P Gipe Wind Power Chelsea Greer Publishing CompanyWhite River Junction VT USA 2003

[9] H Badihi and Y Zhang ldquoPassive fault-tolerant cooperativecontrol in an offshore wind farmrdquo Energy Procedia vol 105pp 2959ndash2964 2017

[10] November 2017 httpsdaskuzawordpresscomarapgir-cografyasi-1

[11] November 2017 httpwwwarapgirgovtrcografi-durumu[12] J Kim and B Yum ldquoSelection between Weibull and log-

normal distributions a comparative simulation studyrdquoComputational Statistics and Data Analysis vol 53 no 2pp 477ndash485 2008

[13] J F Manwell J G Morgan and A L Rogers Wind EnergyExplained 3eory Design and Application John Wiley andSons Ltd West Sussex UK 2002

[14] S Rehman andNM Al-Abbadi ldquoWind shear coefficients andtheir effect on energy productionrdquo Energy Conversion andManagement vol 46 no 15-16 pp 2578ndash2591 2005

[15] H Lettau ldquoNote on aerodynamic roughness-parameter es-timation on the basis of roughness-element distributionrdquoJournal of Applied Meteorology vol 8 no 5 pp 828ndash8321969

[16] E K Akpinar and S Akpinar ldquoAn assessment on seasonalanalysis of wind energy characteristics and wind turbinecharacteristicsrdquo Energy Conversion and Management vol 46no 11-12 pp 1848ndash1867 2005

[17] A Senkal and N S Ccediletin Turkiyersquode kurulu olan buyuk guccedilluruzgar santrallerinin kapasite faktorlerine genel bir bakıs EgeBolgesi Enerji Forumu Denizli Turkey 2009

[18] F Kose andM Ozgoren ldquoRuzgar Enerjisi Potansiyeli Olccedilumuve Ruzgar Turbini seccedilimirdquo Muhendis ve Makine vol 551pp 20ndash30 2005

[19] S Krohn P E Morthorst and S Awerbuch3e Economics ofWind Energy 3e European Wind Energy Association ReportEuropean Wind Energy Association Brussels Belgium 2009

[20] P Nielsen Offshore Wind Energy Projects Feasibility StudyGuidelines SEAWIND-Altener project 41030Z01-2001Ver 30 EMD International AS Aalborg Denmark 2003

[21] C Camporeale Offshore Wind Farms in Italy 3e Sicily CaseOwemes Citavecchia Italy 2006

[22] ODE Study of the Costs of Offshore Wind GenerationmdashAReport to the Renewables Advisory Board (RAB) amp DTI URNNUMBER 07779 Government publication 114 pagesLondon UK 2007

[23] EWEA Financing and Investment Trends 2016 Vol 9 EWEABrussels Belgium May 2017

Advances in Meteorology 9

33OrographicModel In this model on themeasured winddata faults of local impacts caused by the topographicalstructure are being corrected [8] For the remedial of theseimpacts a horizontal scale of 20ndash30 km shall be taken intoaccount Primarily potential current deformations causedby the land topography shall be calculated e speedperturbation is

u ΔX (5)

where X is the potential and u is the three-dimensionalspeed deformation vector

For a given radius R for the potential ow at polarcoordinates the following equation shall be written

Xj KnjJn Cnjr

R( )exp( ln Φ)exp minusCnj

z

R( ) (6)

where Knj is the random coecient Jn n is the level Besselfunction r is the radiusΦ is the azimuth z is the height andCnj jnprime s i which is zero

For the specic problems coecients shall be calculatedfrom surface kinematical limit conditions

Jn Cnjr

R( ) (7)

where Jn n is the level Bessel function Cnj jnprime s i which iszero and r is the radius

Functions are as FourierndashBessel series Knj coecientsmay be dened independently e model forms the graydata on the contour lines on the topographical map Sen-sitivity of the model depends on the density of contours

4 Wind Atlas Analysis andProgram Application

In order to make the wind power more competitive againstother power generation methods bigger wind turbines are

being designed and established on the clusters especially onthe overseas regions called as the wind plant [9]

To be able to establish a wind power plant both me-teorological and nancial parameters are needed is alsoshows that one wind plant establishment actually requires tothink and use more than one discipline at the same time Forthis reason during the analysis WAsP has been used whichhas been already used for the preparation of the EuropeanWind Atlas and Turkish Wind Atlas

is study is aimed at nding out the average wind speedaverage power density energy yield potential obtained asa result of micrositing capacity factor amortization periodand unit cost price required for the establishment of the windpower plant as a result of the performed analysis ese ob-tained values are being planned to be used to generatea compliance factor for the establishment of the wind powerplant by scaling in the rule basis prepared in the fuzzy logicmethod In Figure 1 MatlabSimulink design of the inspectionsystem is seen which is planned to be used as an input unit ofobtained parameters

41 Geographical Status of Arapgir Province Selected for isStudy Mathematical location is between 39deg05prime N latitudeand 38deg30prime E longitude We can examine the province inthree parts in terms of surface features

(i) Mountainous areas in the western and northernparts of the province

(ii) e section east of the district center(iii) Medium-height and slightly rugged Arapgir rubble

forming the southern part [10 11]

As a result of continental climate most part of the areasof the province are coated with steppeis means that forest

Scope 2

Display 2

Display 1

Scope 1

Scope

Display

Amortizationperiod

kWhcost

Unpredictableexpense

Capacityfactor

Averagepower density

Fuzzy logiccontroller

Fuzzy logiccontroller 1

Fuzzy logiccontroller 2

Averagewind speed

0

0

0

0

0

0

Figure 1 MatlabSimulink model of the designed system

Advances in Meteorology 3

land is very small Existing forest lands are not in goodqualityese are mostly very small oak forests Satellite viewtaken from Google Earth of Arapgir Province is shown inFigure 2 In this study topographical obstacles have beentaken into consideration [10 11]

42 Assessment of Wind Measurement Data In this studywind speed and wind direction data belonging to 10m of theheight taken from OMGI (Automatic Meteorological Ob-servation Station) are being used between January 2014 andDecember 2015

Monthly average of the measurements taken at 10m ofthe height in the region is shown in Table 1 When the tableis examined the highest average wind speed is obtained as

41ms in July and the lowest average wind speed is obtainedas 16ms in January

In Table 2 average wind severity and average powerdensity values belonging to the region obtained as a result ofthe analysis made in WAsP are being shown

Twelve sectors have been used in Figure 3 as a result ofthe calculations made in WAsP and the dominant winddirection has been observed as SE at 120deg sector

Most frequently the distribution used for the calculationof wind power potential is the Weibull distribution isdistribution has been found by the Swedish physicistWaloddi Weibull is distribution is considerably exibleand simple and also complies with the real data In otherwords since the Weibull distribution is in compliance withwind speed data it is generally accepted in wind poweranalysis [12] e Weibull distribution function is as follows[13]

hw(V) k

cXV

c( )

kminus1Xe minus(Vc)

k[ ] (8)

where k is the gure parameter (parameter showing the windspeed distribution form) c is the scale parameter (relativecumulative frequency for wind speed) and hw(V) is thepossibility density function of wind speed

In order to nd Weibull parameters (k and c) wind dataof the land are required Analytical and experimentalequations used to nd Weibull parameters are written asfollows [14]

k auU( )minus1086

c UX 0568 +0433k

( )minus1k

(9)

where au is the standard deviation and U is the averagespeed

Arapgir

Figure 2 Satellite view of Arapgir Province

Table 1 Monthly average wind speed measured at 10m height

Month Average wind speed at10m height (ms)

January 16February 2March 26April 28May 31June 38July 41August 36September 29October 19November 25December 22

Table 2 Average wind severity and average power density of theregion

Calculated valuesAverage wind severity (ms) 275Average power density (Wm2) 53

Number of spindles

0

5000

10000

15000

20000NNE

NE

ENE

ESE

SE

SSESSW

SW

WSW

WNW

NW

NNW

Figure 3 Wind rose belonging to Arapgir Province

4 Advances in Meteorology

After examining the wind data in detail and performingthe above-given calculations according to the Weibulldistribution in Figure 4 the average wind speed is found as275ms the average power density is found as 53Wm2 thegure parameter is found as 151 and the scale parameter isfound as 42ms

Wind speed measurements are generally being made atdicenterent heights than at the tower heights of wind turbinesFor this reason these measured wind speeds are being ex-trapolated to turbine tower heights by using the formulaknown as 17 wind power law [14] As known by using theHellmann coecient estimated wind speed values ata requested height may be calculated from the wind speedvalues measured at a specic height Wind speed datameasured at a specic height may be transferred to otherheights by using the following equation

U Urrefh

href( )

μ

(10)

whereU is the wind speed at the height to be calculatedUrrefis the wind speed at the height where the measurementresults are known h is the height of the point to be calculatedfrom the surface href is the height of the point where themeasurement results are known from the surface and μ isthe Hellmann coecient

In this study wind speed values obtained at 10m inArapgir with ten minutes of interval have been moved to80m which is the turbine hub height with the calculatedHellmann coecient

e Hellmann coecient can be taken as 17 in the mostgeneral case However the other ways can be used to de-termine it more accurately [15]

Calculation in terms of roughness length

μ 0096log10z0 + 0016 log10z0( )2 + 024 (11)

Calculation in terms of speed and height

μ 037minus 0088ln Urref( )1minus 0088ln Uref 10( )

(12)

Calculation of roughness length and speed

μ k0 1minus 055log Uref( )[ ] (13)

Equation (13) is proposed by the NASA for the Hell-mann coecient By using the existing data the change ofthe Hellmann coecient as per the direction has been ex-amined in addition to the analysis made according to thewind ow directione average of the Hellmann coecienthas been calculated as 021 with the analysis of 61293 data intotal for all sectors As shown in Figure 5 the highestHellmann coecient has been found in SE which is thedominant wind direction

43 Generation of the Wind Atlas In this study wind atlasstatistics have been prepared belonging to the plant area withthe help of WAsP software by using the frequency distri-bution table obtained from wind speed and direction databelonging to 10 m of the height obstacles in near vicinityroughness data and digitized map with a scale of 125000representing the region topography

Data imported into WAsP are being analyzed and theaverage wind severity map of the region in Figure 6 andpower density map of the area in Figure 7 have been visuallyobtained e wind turbine-locating process shall be carriedout on the points where the wind severity is high by using theaverage wind speed calculated in the region

While planning the installation of wind turbines in thesite wind atlas is being used By using WAsP and wind atlasstatistics locations where the power generation amount willbe high on the digital map may be dened from colourdistribution

44 Micrositing and Turbine Selection In this study it hasbeen planned to establish a wind plant of 12MW in the areaand for this establishment it has been planned to use 2MWof the V80-type turbine belonging to the VESTAS companywithin the WAsP library In order to minimize the in-terference of turbines with each other the micrositing studyhas been performed

During this micrositing study design has been made soas to keep the track area losses of the turbines on each otherin the minimum level Furthermore while designing theoptimum turbine placement so as to obtain a maximumyield wind turbines have been placed on the dominantwind direction not only taking into consideration thegeneration amount but also the operability limits of the

c 42 msk 151U 275 msp 53 Wm2

0 5 10 15 200

25

()

Figure 4 e Weibull distribution belonging to Arapgir Province

00102030405

E S W NEN

EES

EN

EN

NE

NN

WN

W SE SSE

SSW SW

WN

WW

SW

Hellmann coefficient

Figure 5 Change of the Hellmann coecient as per winddirection

Advances in Meteorology 5

turbines in technical terms In Figure 8 the micrositingstudy has been shown where six 2MW VESTAS V80 windturbines are used

Following this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are

used electricity generation of annual gross 41316 GWhand net 38352 GWh has been calculated As a resultof the interference of turbines with each other it hasbeen understood that there will be a 717 loss Annualgeneration values have been shown in Table 3 in themicrositing study where 6 VESTAS V80 wind turbinesare used

In Table 4 generation and loss values for each turbine aregiven for the micrositing study where 6 V80 wind turbineshave been used

As can be shown in Table 4 when annual net powergeneration values are being considered the maximum gen-eration amount 7082GWh was at turbine number 4 and theminimum generation amount 5713GWh was at turbine 6During the micrositing study it has been calculated that themaximumwake loss 979was at turbine 6 and theminimumwake loss 534 was at turbine number 4

45 Capacity Factor of the Area e capacity factor whichis an important parameter and has to be known both bythe generators and consumers is the division of energygenerated in a specific time frame to the maximumenergy that can be generated at that specific time frame[16 17]

In this study the capacity factor has been calculated forthe turbine used during micrositing and turbine selectionstudies and generated micrositings

CF ET

TlowastPR (14)

where CF is the capacity factor ET is the generated totalpower PR is the nominal power value and T is the time

In the micrositing study the annual calculated genera-tion amount for six 2MW V80 wind turbines maximumamount that can be generated from turbines and capacityfactor are shown in Table 5

In case of operation under nominal power of six 2MWturbines during 8760 hours in one year they may generatemaximum 8760times12105120GWh power in one year As

Figure 7 Power density map of the area

Figure 8 Micrositing process of turbines

Table 3 Annual generation values of 6 V80 wind turbines

Parameter Total Min MaxNet annual generation (GWh) 38352 5713 7082Gross annual generation (GWh) 41316 6034 7897Wake (track) loss () 7173 mdash mdash

Figure 6 Average wind severity map calculated in the area

Table 4 Annual generation and loss values of 6 V80 wind turbines

Turbinenumber

Hubheight (m)

Net annualgeneration (GWh)

Wake(track) loss

Turbine 1 80 6789 709Turbine 2 80 6145 618Turbine 3 80 5824 962Turbine 4 80 7082 534Turbine 5 80 6799 829Turbine 6 80 5713 979

6 Advances in Meteorology

a result of the calculations made in WAsP it has beendetermined that the wind power generated by the windpower plant formed by six 2MW turbines shall generate38352GWh power in one year and the capacity factor shallbe realized as 38352105120 03648 in other words3648 e capacity factor which is calculated at and above25 for the wind turbines deemed appropriate for the in-vestment in Turkey [18]

46 Economical Analysis of the Data One of the most im-portant studies that have to be carried out while establishinga wind turbine to a region is the calculation of kWh powercost Generally the cost of one wind power project per kWhis found by proportioning the annual total cost to the annualpower generation amount

e annual power generation amount changesdepending on the parameters such as the hub height ofturbine rotor diameter average wind severity of the areaand annual cost may be correlated with turbine priceturbine foundations inner site road construction costsinvestment costs and project lifetime In Figure 9 the coststructure is shown per kWh for wind power projects [19]

In this study within the rst establishment turbine costnetwork connection foundation and establishment costselectricity installation and road construction and control

Table 5 Capacity factor of the area

Turbinemodel

Turbinenumber

Installedpower

Annual calculated production amount(GWh)

Nominal power to be produced(GWh)

Capacity factor()

VESTAS V80 6 12 38352 10512 3648

Turbineand

installation

Project lifeCapital cost

pa

Turbine price inturbine-based roads

Rotor diameterhub height Average wind speed

region properties

Annual capitalcosts

Total annualcosts

Energy costper kWh

Annual energyproduction

Annual operatingand

maintenance costs

kwh

kwh

Figure 9 Cost structure per kWh for wind power projects

Table 6 Turbine establishment cost [20ndash26]

Establishment parameters Cost (euro)Turbine and installation 9000000Project preparation costs (permission and licences) 650000Land rental cost 100000Network connections (cable transformer andcommunication) 1800000

Foundation and installation (foundation roadand site) 1200000

Finance (consultancy insurance and bank) 450000Total 13200000

Table 7 Annual income-expense table [20ndash26]

euroyearElectricity sales income 2109360Emission sales income 82608Annual operational income (euroyear) 2191168Rental amount 100000Maintenance-repair insurance 130000Labour 30000Annual licence cost 1000General administrative costs 5000Annual operational expenses (euroyear) 266000Annual operational margin (euroyear) 1925168

Advances in Meteorology 7

systems have been considered as mentioned in the report ofthe European Wind Power Union and land rental con-sultancy maintenance and repair and yearly licence costshave been included into the analysis as permanently paidcosts within years (Table 6)

Annual 38352GWh power generation has been calcu-lated for the wind power plant formed with six 2MWVESTAS V80 wind turbines In this case annual income hasbeen calculated as 2109360 euro with 55 eurocentkWh electricitysales price Emission sales income for the 38352GWh powergeneration has been calculated as 82608 euro annually Asa result of the calculations made annual income and ex-penses as well as the annual margin are shown in Table 7

In this study operational expenses maintenance repairinsurance rental and licence costs and some valueschanging as per year such as interest have been re ected asaverage values in the income-expense table by taking intoconsideration the project lifetime In case of selling theelectricity at 55 eurocentkWh at the wind power plant formedby 6 VESTAS V80 wind turbines it is seen that the cash owwill pass to the positive state at 7 years and the projectreturns at year 7

In this study cost per kWh for the capacity factor ob-tained from selected turbines has been calculated By usingthe capacity factor as 3648 calculated for the VESTAS V80turbine used in the established system the annual generationamount of the plant in kWh has been found

In order to calculate the unit cost of electricity powerobtained from wind turbines it is necessary to know theregaining factor of investment e regaining factor ofcapital (C) is calculated with the following formula

C i(1 + i)n

(1 + i)n minus 1 (15)

where i is the interest rate () and n is the turbine lifetime(years)

Generation cost is as follows

euroU CT(C + I)

E (16)

where euroU is the generation cost (eurokWh) I is the servicemaintenance and insurance (operational) expenses () CTis the total establishment cost of the wind turbine (euro) and Eis the annual generated power amount (kWh)

e power to be generated by the wind turbine(kWhyear) in the region (E) where the establishment of thewind turbine is planned is dened with the followingformula

E ηkay middot Cp middot12middot ρh middot S middotsum

ki1Δti middot U3

r (17)

where Δti is the time interval (h) U is the wind speed (ms)ρh is the air density (kgm

3) r is the radius S is the scannedarea by the wind turbine (m2) Cp is the power coecientηkay is the fraction losses at bearings (eg 0996) losses at thegear box (eg 0972) and losses at the electricity generator(eg 094) and general total coecient (may be consideredas approximately ηkay 09 at calculations)

C 0075(1 + 0075)25

(1 + 0075)25 minus 1 008971

U 13200000(008971 + 002)

33955200 00426 euroKWh

426 eurocKWh

(18)

In the calculations made unit cost values to be used asthe input unit in the fuzzy logic system are found as426 eurockWh

71

309

3648

2

426

7Amortization

period

kWhcost

Unpredictableexpense

Capacityfactor

Averagepower density

Averagewind speed

Fuzzy logiccontroller

Fuzzy logiccontroller 2

Display 1

Scope 1

Scope

Display

Display 2

Scope 2

Fuzzy logiccontroller 1

847256

835742

775063

Figure 10 Display of the result values generated by the fuzzy-based decision-making system designed according to the input valuescalculated by the WAsP

8 Advances in Meteorology

5 Conclusion

In this study average wind speed and wind direction data ofArapgir province measured at 10m height and 10-minuteintervals and taken from OMGI (Automatic MeteorologicalObservation Station) were analyzed by WAsP software Inthe analysis made the average wind speed is found as275ms the average power density is found as 53Wm2 thefigure parameter is found as 151 and the scale parameter isfound as 42ms obtained at 10m according to the Weibulldistribution

As a result of the calculations made in WAsP 12 sectorshave been used in the wind mill and dominant wind di-rection has been observed as SE at 120deg sector e change ofthe Hellmann coefficient as per the direction by usingexisting data in addition to the analysis made as per the windblowing has been examined and the average Hellmanncoefficient has been calculated as 034

As a result of this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are usedwith the wind power plant to be established in the region ithas been calculated that annual gross 41316GWh and net38352GWh electricity shall be generated It has been un-derstood that 717 loss shall occur as a result of the in-terference of wind turbines with each other

In case six 2MW turbines operate under nominal powerduring 8760 hours a maximum of 105120GWh energy maybe generated As a result of the calculations made in WAsPit has been determined that the wind power plant formed bysix 2MW turbines shall generate 38352GWh energy in oneyear and the capacity factor shall be realized as 3648

Annual generation values of the plant have been found as426 eurockWh by using 3648 capacity factor calculated forthe VESTAS V80 turbine used in the established systemeamortization period is calculated as 7 years Unpredictableexpenditure was selected as 2 of the initial installation coste height of the tower of the wind turbine was determinedas 80m e wind speed at 80m height was calculated as71ms e power density at the height of 80m was cal-culated as 309 In Figure 10 the final relevance factor ofrunning the system in the MatlabSimulink model is 8357

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors certify that there are no actual or potentialconflicts of interest in relation to this article

References

[1] October 2017 httpswwwmgmgovtrgenelruzgar-atlasiaspx[2] A Bechkaoui A Ameur S Bouras and A Hadjadj ldquoOpen-

circuit and inter-turn short-circuit detection in PMSG forwind turbine applications using fuzzy logicrdquo Energy Procediavol 74 pp 1323ndash1336 2015

[3] E T Karagol and I KavazDunyada ve Turkiyersquode YenilenebilirEnerji Turkuvaz Haberlesme ve Yayıncılık AS Vol 197Istanbul Turkey 2017

[4] E Kovac-Andriḉ T Radanoviḉ I Topaloviḉ B Markoviḉand N Sakaḉ ldquoTemporal variations in concentrations ofozone nitrogen dioxide and carbon monoxide at OsijekCroatiardquo Advances inMeteorology vol 2013 Article ID 4697867 pages 2013

[5] J L Pearce J Beringer N Nicholls R J Hyndman andN J Tapper ldquoQuantifying the influence of local meteorologyon air quality using generalized additive modelsrdquoAtmosphericEnvironment vol 45 no 6 pp 1328ndash1336 2011

[6] S Mathew Wind Energy Fundamentals Resource Analysisand Economics Springer Berlin Germany 2006

[7] S Mentes Ruzgar Enerjisi ve Sistemleri Ders Notları ITUUccedilak Uzay Fakultesi Istanbul Turkey 2008

[8] P Gipe Wind Power Chelsea Greer Publishing CompanyWhite River Junction VT USA 2003

[9] H Badihi and Y Zhang ldquoPassive fault-tolerant cooperativecontrol in an offshore wind farmrdquo Energy Procedia vol 105pp 2959ndash2964 2017

[10] November 2017 httpsdaskuzawordpresscomarapgir-cografyasi-1

[11] November 2017 httpwwwarapgirgovtrcografi-durumu[12] J Kim and B Yum ldquoSelection between Weibull and log-

normal distributions a comparative simulation studyrdquoComputational Statistics and Data Analysis vol 53 no 2pp 477ndash485 2008

[13] J F Manwell J G Morgan and A L Rogers Wind EnergyExplained 3eory Design and Application John Wiley andSons Ltd West Sussex UK 2002

[14] S Rehman andNM Al-Abbadi ldquoWind shear coefficients andtheir effect on energy productionrdquo Energy Conversion andManagement vol 46 no 15-16 pp 2578ndash2591 2005

[15] H Lettau ldquoNote on aerodynamic roughness-parameter es-timation on the basis of roughness-element distributionrdquoJournal of Applied Meteorology vol 8 no 5 pp 828ndash8321969

[16] E K Akpinar and S Akpinar ldquoAn assessment on seasonalanalysis of wind energy characteristics and wind turbinecharacteristicsrdquo Energy Conversion and Management vol 46no 11-12 pp 1848ndash1867 2005

[17] A Senkal and N S Ccediletin Turkiyersquode kurulu olan buyuk guccedilluruzgar santrallerinin kapasite faktorlerine genel bir bakıs EgeBolgesi Enerji Forumu Denizli Turkey 2009

[18] F Kose andM Ozgoren ldquoRuzgar Enerjisi Potansiyeli Olccedilumuve Ruzgar Turbini seccedilimirdquo Muhendis ve Makine vol 551pp 20ndash30 2005

[19] S Krohn P E Morthorst and S Awerbuch3e Economics ofWind Energy 3e European Wind Energy Association ReportEuropean Wind Energy Association Brussels Belgium 2009

[20] P Nielsen Offshore Wind Energy Projects Feasibility StudyGuidelines SEAWIND-Altener project 41030Z01-2001Ver 30 EMD International AS Aalborg Denmark 2003

[21] C Camporeale Offshore Wind Farms in Italy 3e Sicily CaseOwemes Citavecchia Italy 2006

[22] ODE Study of the Costs of Offshore Wind GenerationmdashAReport to the Renewables Advisory Board (RAB) amp DTI URNNUMBER 07779 Government publication 114 pagesLondon UK 2007

[23] EWEA Financing and Investment Trends 2016 Vol 9 EWEABrussels Belgium May 2017

Advances in Meteorology 9

land is very small Existing forest lands are not in goodqualityese are mostly very small oak forests Satellite viewtaken from Google Earth of Arapgir Province is shown inFigure 2 In this study topographical obstacles have beentaken into consideration [10 11]

42 Assessment of Wind Measurement Data In this studywind speed and wind direction data belonging to 10m of theheight taken from OMGI (Automatic Meteorological Ob-servation Station) are being used between January 2014 andDecember 2015

Monthly average of the measurements taken at 10m ofthe height in the region is shown in Table 1 When the tableis examined the highest average wind speed is obtained as

41ms in July and the lowest average wind speed is obtainedas 16ms in January

In Table 2 average wind severity and average powerdensity values belonging to the region obtained as a result ofthe analysis made in WAsP are being shown

Twelve sectors have been used in Figure 3 as a result ofthe calculations made in WAsP and the dominant winddirection has been observed as SE at 120deg sector

Most frequently the distribution used for the calculationof wind power potential is the Weibull distribution isdistribution has been found by the Swedish physicistWaloddi Weibull is distribution is considerably exibleand simple and also complies with the real data In otherwords since the Weibull distribution is in compliance withwind speed data it is generally accepted in wind poweranalysis [12] e Weibull distribution function is as follows[13]

hw(V) k

cXV

c( )

kminus1Xe minus(Vc)

k[ ] (8)

where k is the gure parameter (parameter showing the windspeed distribution form) c is the scale parameter (relativecumulative frequency for wind speed) and hw(V) is thepossibility density function of wind speed

In order to nd Weibull parameters (k and c) wind dataof the land are required Analytical and experimentalequations used to nd Weibull parameters are written asfollows [14]

k auU( )minus1086

c UX 0568 +0433k

( )minus1k

(9)

where au is the standard deviation and U is the averagespeed

Arapgir

Figure 2 Satellite view of Arapgir Province

Table 1 Monthly average wind speed measured at 10m height

Month Average wind speed at10m height (ms)

January 16February 2March 26April 28May 31June 38July 41August 36September 29October 19November 25December 22

Table 2 Average wind severity and average power density of theregion

Calculated valuesAverage wind severity (ms) 275Average power density (Wm2) 53

Number of spindles

0

5000

10000

15000

20000NNE

NE

ENE

ESE

SE

SSESSW

SW

WSW

WNW

NW

NNW

Figure 3 Wind rose belonging to Arapgir Province

4 Advances in Meteorology

After examining the wind data in detail and performingthe above-given calculations according to the Weibulldistribution in Figure 4 the average wind speed is found as275ms the average power density is found as 53Wm2 thegure parameter is found as 151 and the scale parameter isfound as 42ms

Wind speed measurements are generally being made atdicenterent heights than at the tower heights of wind turbinesFor this reason these measured wind speeds are being ex-trapolated to turbine tower heights by using the formulaknown as 17 wind power law [14] As known by using theHellmann coecient estimated wind speed values ata requested height may be calculated from the wind speedvalues measured at a specic height Wind speed datameasured at a specic height may be transferred to otherheights by using the following equation

U Urrefh

href( )

μ

(10)

whereU is the wind speed at the height to be calculatedUrrefis the wind speed at the height where the measurementresults are known h is the height of the point to be calculatedfrom the surface href is the height of the point where themeasurement results are known from the surface and μ isthe Hellmann coecient

In this study wind speed values obtained at 10m inArapgir with ten minutes of interval have been moved to80m which is the turbine hub height with the calculatedHellmann coecient

e Hellmann coecient can be taken as 17 in the mostgeneral case However the other ways can be used to de-termine it more accurately [15]

Calculation in terms of roughness length

μ 0096log10z0 + 0016 log10z0( )2 + 024 (11)

Calculation in terms of speed and height

μ 037minus 0088ln Urref( )1minus 0088ln Uref 10( )

(12)

Calculation of roughness length and speed

μ k0 1minus 055log Uref( )[ ] (13)

Equation (13) is proposed by the NASA for the Hell-mann coecient By using the existing data the change ofthe Hellmann coecient as per the direction has been ex-amined in addition to the analysis made according to thewind ow directione average of the Hellmann coecienthas been calculated as 021 with the analysis of 61293 data intotal for all sectors As shown in Figure 5 the highestHellmann coecient has been found in SE which is thedominant wind direction

43 Generation of the Wind Atlas In this study wind atlasstatistics have been prepared belonging to the plant area withthe help of WAsP software by using the frequency distri-bution table obtained from wind speed and direction databelonging to 10 m of the height obstacles in near vicinityroughness data and digitized map with a scale of 125000representing the region topography

Data imported into WAsP are being analyzed and theaverage wind severity map of the region in Figure 6 andpower density map of the area in Figure 7 have been visuallyobtained e wind turbine-locating process shall be carriedout on the points where the wind severity is high by using theaverage wind speed calculated in the region

While planning the installation of wind turbines in thesite wind atlas is being used By using WAsP and wind atlasstatistics locations where the power generation amount willbe high on the digital map may be dened from colourdistribution

44 Micrositing and Turbine Selection In this study it hasbeen planned to establish a wind plant of 12MW in the areaand for this establishment it has been planned to use 2MWof the V80-type turbine belonging to the VESTAS companywithin the WAsP library In order to minimize the in-terference of turbines with each other the micrositing studyhas been performed

During this micrositing study design has been made soas to keep the track area losses of the turbines on each otherin the minimum level Furthermore while designing theoptimum turbine placement so as to obtain a maximumyield wind turbines have been placed on the dominantwind direction not only taking into consideration thegeneration amount but also the operability limits of the

c 42 msk 151U 275 msp 53 Wm2

0 5 10 15 200

25

()

Figure 4 e Weibull distribution belonging to Arapgir Province

00102030405

E S W NEN

EES

EN

EN

NE

NN

WN

W SE SSE

SSW SW

WN

WW

SW

Hellmann coefficient

Figure 5 Change of the Hellmann coecient as per winddirection

Advances in Meteorology 5

turbines in technical terms In Figure 8 the micrositingstudy has been shown where six 2MW VESTAS V80 windturbines are used

Following this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are

used electricity generation of annual gross 41316 GWhand net 38352 GWh has been calculated As a resultof the interference of turbines with each other it hasbeen understood that there will be a 717 loss Annualgeneration values have been shown in Table 3 in themicrositing study where 6 VESTAS V80 wind turbinesare used

In Table 4 generation and loss values for each turbine aregiven for the micrositing study where 6 V80 wind turbineshave been used

As can be shown in Table 4 when annual net powergeneration values are being considered the maximum gen-eration amount 7082GWh was at turbine number 4 and theminimum generation amount 5713GWh was at turbine 6During the micrositing study it has been calculated that themaximumwake loss 979was at turbine 6 and theminimumwake loss 534 was at turbine number 4

45 Capacity Factor of the Area e capacity factor whichis an important parameter and has to be known both bythe generators and consumers is the division of energygenerated in a specific time frame to the maximumenergy that can be generated at that specific time frame[16 17]

In this study the capacity factor has been calculated forthe turbine used during micrositing and turbine selectionstudies and generated micrositings

CF ET

TlowastPR (14)

where CF is the capacity factor ET is the generated totalpower PR is the nominal power value and T is the time

In the micrositing study the annual calculated genera-tion amount for six 2MW V80 wind turbines maximumamount that can be generated from turbines and capacityfactor are shown in Table 5

In case of operation under nominal power of six 2MWturbines during 8760 hours in one year they may generatemaximum 8760times12105120GWh power in one year As

Figure 7 Power density map of the area

Figure 8 Micrositing process of turbines

Table 3 Annual generation values of 6 V80 wind turbines

Parameter Total Min MaxNet annual generation (GWh) 38352 5713 7082Gross annual generation (GWh) 41316 6034 7897Wake (track) loss () 7173 mdash mdash

Figure 6 Average wind severity map calculated in the area

Table 4 Annual generation and loss values of 6 V80 wind turbines

Turbinenumber

Hubheight (m)

Net annualgeneration (GWh)

Wake(track) loss

Turbine 1 80 6789 709Turbine 2 80 6145 618Turbine 3 80 5824 962Turbine 4 80 7082 534Turbine 5 80 6799 829Turbine 6 80 5713 979

6 Advances in Meteorology

a result of the calculations made in WAsP it has beendetermined that the wind power generated by the windpower plant formed by six 2MW turbines shall generate38352GWh power in one year and the capacity factor shallbe realized as 38352105120 03648 in other words3648 e capacity factor which is calculated at and above25 for the wind turbines deemed appropriate for the in-vestment in Turkey [18]

46 Economical Analysis of the Data One of the most im-portant studies that have to be carried out while establishinga wind turbine to a region is the calculation of kWh powercost Generally the cost of one wind power project per kWhis found by proportioning the annual total cost to the annualpower generation amount

e annual power generation amount changesdepending on the parameters such as the hub height ofturbine rotor diameter average wind severity of the areaand annual cost may be correlated with turbine priceturbine foundations inner site road construction costsinvestment costs and project lifetime In Figure 9 the coststructure is shown per kWh for wind power projects [19]

In this study within the rst establishment turbine costnetwork connection foundation and establishment costselectricity installation and road construction and control

Table 5 Capacity factor of the area

Turbinemodel

Turbinenumber

Installedpower

Annual calculated production amount(GWh)

Nominal power to be produced(GWh)

Capacity factor()

VESTAS V80 6 12 38352 10512 3648

Turbineand

installation

Project lifeCapital cost

pa

Turbine price inturbine-based roads

Rotor diameterhub height Average wind speed

region properties

Annual capitalcosts

Total annualcosts

Energy costper kWh

Annual energyproduction

Annual operatingand

maintenance costs

kwh

kwh

Figure 9 Cost structure per kWh for wind power projects

Table 6 Turbine establishment cost [20ndash26]

Establishment parameters Cost (euro)Turbine and installation 9000000Project preparation costs (permission and licences) 650000Land rental cost 100000Network connections (cable transformer andcommunication) 1800000

Foundation and installation (foundation roadand site) 1200000

Finance (consultancy insurance and bank) 450000Total 13200000

Table 7 Annual income-expense table [20ndash26]

euroyearElectricity sales income 2109360Emission sales income 82608Annual operational income (euroyear) 2191168Rental amount 100000Maintenance-repair insurance 130000Labour 30000Annual licence cost 1000General administrative costs 5000Annual operational expenses (euroyear) 266000Annual operational margin (euroyear) 1925168

Advances in Meteorology 7

systems have been considered as mentioned in the report ofthe European Wind Power Union and land rental con-sultancy maintenance and repair and yearly licence costshave been included into the analysis as permanently paidcosts within years (Table 6)

Annual 38352GWh power generation has been calcu-lated for the wind power plant formed with six 2MWVESTAS V80 wind turbines In this case annual income hasbeen calculated as 2109360 euro with 55 eurocentkWh electricitysales price Emission sales income for the 38352GWh powergeneration has been calculated as 82608 euro annually Asa result of the calculations made annual income and ex-penses as well as the annual margin are shown in Table 7

In this study operational expenses maintenance repairinsurance rental and licence costs and some valueschanging as per year such as interest have been re ected asaverage values in the income-expense table by taking intoconsideration the project lifetime In case of selling theelectricity at 55 eurocentkWh at the wind power plant formedby 6 VESTAS V80 wind turbines it is seen that the cash owwill pass to the positive state at 7 years and the projectreturns at year 7

In this study cost per kWh for the capacity factor ob-tained from selected turbines has been calculated By usingthe capacity factor as 3648 calculated for the VESTAS V80turbine used in the established system the annual generationamount of the plant in kWh has been found

In order to calculate the unit cost of electricity powerobtained from wind turbines it is necessary to know theregaining factor of investment e regaining factor ofcapital (C) is calculated with the following formula

C i(1 + i)n

(1 + i)n minus 1 (15)

where i is the interest rate () and n is the turbine lifetime(years)

Generation cost is as follows

euroU CT(C + I)

E (16)

where euroU is the generation cost (eurokWh) I is the servicemaintenance and insurance (operational) expenses () CTis the total establishment cost of the wind turbine (euro) and Eis the annual generated power amount (kWh)

e power to be generated by the wind turbine(kWhyear) in the region (E) where the establishment of thewind turbine is planned is dened with the followingformula

E ηkay middot Cp middot12middot ρh middot S middotsum

ki1Δti middot U3

r (17)

where Δti is the time interval (h) U is the wind speed (ms)ρh is the air density (kgm

3) r is the radius S is the scannedarea by the wind turbine (m2) Cp is the power coecientηkay is the fraction losses at bearings (eg 0996) losses at thegear box (eg 0972) and losses at the electricity generator(eg 094) and general total coecient (may be consideredas approximately ηkay 09 at calculations)

C 0075(1 + 0075)25

(1 + 0075)25 minus 1 008971

U 13200000(008971 + 002)

33955200 00426 euroKWh

426 eurocKWh

(18)

In the calculations made unit cost values to be used asthe input unit in the fuzzy logic system are found as426 eurockWh

71

309

3648

2

426

7Amortization

period

kWhcost

Unpredictableexpense

Capacityfactor

Averagepower density

Averagewind speed

Fuzzy logiccontroller

Fuzzy logiccontroller 2

Display 1

Scope 1

Scope

Display

Display 2

Scope 2

Fuzzy logiccontroller 1

847256

835742

775063

Figure 10 Display of the result values generated by the fuzzy-based decision-making system designed according to the input valuescalculated by the WAsP

8 Advances in Meteorology

5 Conclusion

In this study average wind speed and wind direction data ofArapgir province measured at 10m height and 10-minuteintervals and taken from OMGI (Automatic MeteorologicalObservation Station) were analyzed by WAsP software Inthe analysis made the average wind speed is found as275ms the average power density is found as 53Wm2 thefigure parameter is found as 151 and the scale parameter isfound as 42ms obtained at 10m according to the Weibulldistribution

As a result of the calculations made in WAsP 12 sectorshave been used in the wind mill and dominant wind di-rection has been observed as SE at 120deg sector e change ofthe Hellmann coefficient as per the direction by usingexisting data in addition to the analysis made as per the windblowing has been examined and the average Hellmanncoefficient has been calculated as 034

As a result of this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are usedwith the wind power plant to be established in the region ithas been calculated that annual gross 41316GWh and net38352GWh electricity shall be generated It has been un-derstood that 717 loss shall occur as a result of the in-terference of wind turbines with each other

In case six 2MW turbines operate under nominal powerduring 8760 hours a maximum of 105120GWh energy maybe generated As a result of the calculations made in WAsPit has been determined that the wind power plant formed bysix 2MW turbines shall generate 38352GWh energy in oneyear and the capacity factor shall be realized as 3648

Annual generation values of the plant have been found as426 eurockWh by using 3648 capacity factor calculated forthe VESTAS V80 turbine used in the established systemeamortization period is calculated as 7 years Unpredictableexpenditure was selected as 2 of the initial installation coste height of the tower of the wind turbine was determinedas 80m e wind speed at 80m height was calculated as71ms e power density at the height of 80m was cal-culated as 309 In Figure 10 the final relevance factor ofrunning the system in the MatlabSimulink model is 8357

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors certify that there are no actual or potentialconflicts of interest in relation to this article

References

[1] October 2017 httpswwwmgmgovtrgenelruzgar-atlasiaspx[2] A Bechkaoui A Ameur S Bouras and A Hadjadj ldquoOpen-

circuit and inter-turn short-circuit detection in PMSG forwind turbine applications using fuzzy logicrdquo Energy Procediavol 74 pp 1323ndash1336 2015

[3] E T Karagol and I KavazDunyada ve Turkiyersquode YenilenebilirEnerji Turkuvaz Haberlesme ve Yayıncılık AS Vol 197Istanbul Turkey 2017

[4] E Kovac-Andriḉ T Radanoviḉ I Topaloviḉ B Markoviḉand N Sakaḉ ldquoTemporal variations in concentrations ofozone nitrogen dioxide and carbon monoxide at OsijekCroatiardquo Advances inMeteorology vol 2013 Article ID 4697867 pages 2013

[5] J L Pearce J Beringer N Nicholls R J Hyndman andN J Tapper ldquoQuantifying the influence of local meteorologyon air quality using generalized additive modelsrdquoAtmosphericEnvironment vol 45 no 6 pp 1328ndash1336 2011

[6] S Mathew Wind Energy Fundamentals Resource Analysisand Economics Springer Berlin Germany 2006

[7] S Mentes Ruzgar Enerjisi ve Sistemleri Ders Notları ITUUccedilak Uzay Fakultesi Istanbul Turkey 2008

[8] P Gipe Wind Power Chelsea Greer Publishing CompanyWhite River Junction VT USA 2003

[9] H Badihi and Y Zhang ldquoPassive fault-tolerant cooperativecontrol in an offshore wind farmrdquo Energy Procedia vol 105pp 2959ndash2964 2017

[10] November 2017 httpsdaskuzawordpresscomarapgir-cografyasi-1

[11] November 2017 httpwwwarapgirgovtrcografi-durumu[12] J Kim and B Yum ldquoSelection between Weibull and log-

normal distributions a comparative simulation studyrdquoComputational Statistics and Data Analysis vol 53 no 2pp 477ndash485 2008

[13] J F Manwell J G Morgan and A L Rogers Wind EnergyExplained 3eory Design and Application John Wiley andSons Ltd West Sussex UK 2002

[14] S Rehman andNM Al-Abbadi ldquoWind shear coefficients andtheir effect on energy productionrdquo Energy Conversion andManagement vol 46 no 15-16 pp 2578ndash2591 2005

[15] H Lettau ldquoNote on aerodynamic roughness-parameter es-timation on the basis of roughness-element distributionrdquoJournal of Applied Meteorology vol 8 no 5 pp 828ndash8321969

[16] E K Akpinar and S Akpinar ldquoAn assessment on seasonalanalysis of wind energy characteristics and wind turbinecharacteristicsrdquo Energy Conversion and Management vol 46no 11-12 pp 1848ndash1867 2005

[17] A Senkal and N S Ccediletin Turkiyersquode kurulu olan buyuk guccedilluruzgar santrallerinin kapasite faktorlerine genel bir bakıs EgeBolgesi Enerji Forumu Denizli Turkey 2009

[18] F Kose andM Ozgoren ldquoRuzgar Enerjisi Potansiyeli Olccedilumuve Ruzgar Turbini seccedilimirdquo Muhendis ve Makine vol 551pp 20ndash30 2005

[19] S Krohn P E Morthorst and S Awerbuch3e Economics ofWind Energy 3e European Wind Energy Association ReportEuropean Wind Energy Association Brussels Belgium 2009

[20] P Nielsen Offshore Wind Energy Projects Feasibility StudyGuidelines SEAWIND-Altener project 41030Z01-2001Ver 30 EMD International AS Aalborg Denmark 2003

[21] C Camporeale Offshore Wind Farms in Italy 3e Sicily CaseOwemes Citavecchia Italy 2006

[22] ODE Study of the Costs of Offshore Wind GenerationmdashAReport to the Renewables Advisory Board (RAB) amp DTI URNNUMBER 07779 Government publication 114 pagesLondon UK 2007

[23] EWEA Financing and Investment Trends 2016 Vol 9 EWEABrussels Belgium May 2017

Advances in Meteorology 9

After examining the wind data in detail and performingthe above-given calculations according to the Weibulldistribution in Figure 4 the average wind speed is found as275ms the average power density is found as 53Wm2 thegure parameter is found as 151 and the scale parameter isfound as 42ms

Wind speed measurements are generally being made atdicenterent heights than at the tower heights of wind turbinesFor this reason these measured wind speeds are being ex-trapolated to turbine tower heights by using the formulaknown as 17 wind power law [14] As known by using theHellmann coecient estimated wind speed values ata requested height may be calculated from the wind speedvalues measured at a specic height Wind speed datameasured at a specic height may be transferred to otherheights by using the following equation

U Urrefh

href( )

μ

(10)

whereU is the wind speed at the height to be calculatedUrrefis the wind speed at the height where the measurementresults are known h is the height of the point to be calculatedfrom the surface href is the height of the point where themeasurement results are known from the surface and μ isthe Hellmann coecient

In this study wind speed values obtained at 10m inArapgir with ten minutes of interval have been moved to80m which is the turbine hub height with the calculatedHellmann coecient

e Hellmann coecient can be taken as 17 in the mostgeneral case However the other ways can be used to de-termine it more accurately [15]

Calculation in terms of roughness length

μ 0096log10z0 + 0016 log10z0( )2 + 024 (11)

Calculation in terms of speed and height

μ 037minus 0088ln Urref( )1minus 0088ln Uref 10( )

(12)

Calculation of roughness length and speed

μ k0 1minus 055log Uref( )[ ] (13)

Equation (13) is proposed by the NASA for the Hell-mann coecient By using the existing data the change ofthe Hellmann coecient as per the direction has been ex-amined in addition to the analysis made according to thewind ow directione average of the Hellmann coecienthas been calculated as 021 with the analysis of 61293 data intotal for all sectors As shown in Figure 5 the highestHellmann coecient has been found in SE which is thedominant wind direction

43 Generation of the Wind Atlas In this study wind atlasstatistics have been prepared belonging to the plant area withthe help of WAsP software by using the frequency distri-bution table obtained from wind speed and direction databelonging to 10 m of the height obstacles in near vicinityroughness data and digitized map with a scale of 125000representing the region topography

Data imported into WAsP are being analyzed and theaverage wind severity map of the region in Figure 6 andpower density map of the area in Figure 7 have been visuallyobtained e wind turbine-locating process shall be carriedout on the points where the wind severity is high by using theaverage wind speed calculated in the region

While planning the installation of wind turbines in thesite wind atlas is being used By using WAsP and wind atlasstatistics locations where the power generation amount willbe high on the digital map may be dened from colourdistribution

44 Micrositing and Turbine Selection In this study it hasbeen planned to establish a wind plant of 12MW in the areaand for this establishment it has been planned to use 2MWof the V80-type turbine belonging to the VESTAS companywithin the WAsP library In order to minimize the in-terference of turbines with each other the micrositing studyhas been performed

During this micrositing study design has been made soas to keep the track area losses of the turbines on each otherin the minimum level Furthermore while designing theoptimum turbine placement so as to obtain a maximumyield wind turbines have been placed on the dominantwind direction not only taking into consideration thegeneration amount but also the operability limits of the

c 42 msk 151U 275 msp 53 Wm2

0 5 10 15 200

25

()

Figure 4 e Weibull distribution belonging to Arapgir Province

00102030405

E S W NEN

EES

EN

EN

NE

NN

WN

W SE SSE

SSW SW

WN

WW

SW

Hellmann coefficient

Figure 5 Change of the Hellmann coecient as per winddirection

Advances in Meteorology 5

turbines in technical terms In Figure 8 the micrositingstudy has been shown where six 2MW VESTAS V80 windturbines are used

Following this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are

used electricity generation of annual gross 41316 GWhand net 38352 GWh has been calculated As a resultof the interference of turbines with each other it hasbeen understood that there will be a 717 loss Annualgeneration values have been shown in Table 3 in themicrositing study where 6 VESTAS V80 wind turbinesare used

In Table 4 generation and loss values for each turbine aregiven for the micrositing study where 6 V80 wind turbineshave been used

As can be shown in Table 4 when annual net powergeneration values are being considered the maximum gen-eration amount 7082GWh was at turbine number 4 and theminimum generation amount 5713GWh was at turbine 6During the micrositing study it has been calculated that themaximumwake loss 979was at turbine 6 and theminimumwake loss 534 was at turbine number 4

45 Capacity Factor of the Area e capacity factor whichis an important parameter and has to be known both bythe generators and consumers is the division of energygenerated in a specific time frame to the maximumenergy that can be generated at that specific time frame[16 17]

In this study the capacity factor has been calculated forthe turbine used during micrositing and turbine selectionstudies and generated micrositings

CF ET

TlowastPR (14)

where CF is the capacity factor ET is the generated totalpower PR is the nominal power value and T is the time

In the micrositing study the annual calculated genera-tion amount for six 2MW V80 wind turbines maximumamount that can be generated from turbines and capacityfactor are shown in Table 5

In case of operation under nominal power of six 2MWturbines during 8760 hours in one year they may generatemaximum 8760times12105120GWh power in one year As

Figure 7 Power density map of the area

Figure 8 Micrositing process of turbines

Table 3 Annual generation values of 6 V80 wind turbines

Parameter Total Min MaxNet annual generation (GWh) 38352 5713 7082Gross annual generation (GWh) 41316 6034 7897Wake (track) loss () 7173 mdash mdash

Figure 6 Average wind severity map calculated in the area

Table 4 Annual generation and loss values of 6 V80 wind turbines

Turbinenumber

Hubheight (m)

Net annualgeneration (GWh)

Wake(track) loss

Turbine 1 80 6789 709Turbine 2 80 6145 618Turbine 3 80 5824 962Turbine 4 80 7082 534Turbine 5 80 6799 829Turbine 6 80 5713 979

6 Advances in Meteorology

a result of the calculations made in WAsP it has beendetermined that the wind power generated by the windpower plant formed by six 2MW turbines shall generate38352GWh power in one year and the capacity factor shallbe realized as 38352105120 03648 in other words3648 e capacity factor which is calculated at and above25 for the wind turbines deemed appropriate for the in-vestment in Turkey [18]

46 Economical Analysis of the Data One of the most im-portant studies that have to be carried out while establishinga wind turbine to a region is the calculation of kWh powercost Generally the cost of one wind power project per kWhis found by proportioning the annual total cost to the annualpower generation amount

e annual power generation amount changesdepending on the parameters such as the hub height ofturbine rotor diameter average wind severity of the areaand annual cost may be correlated with turbine priceturbine foundations inner site road construction costsinvestment costs and project lifetime In Figure 9 the coststructure is shown per kWh for wind power projects [19]

In this study within the rst establishment turbine costnetwork connection foundation and establishment costselectricity installation and road construction and control

Table 5 Capacity factor of the area

Turbinemodel

Turbinenumber

Installedpower

Annual calculated production amount(GWh)

Nominal power to be produced(GWh)

Capacity factor()

VESTAS V80 6 12 38352 10512 3648

Turbineand

installation

Project lifeCapital cost

pa

Turbine price inturbine-based roads

Rotor diameterhub height Average wind speed

region properties

Annual capitalcosts

Total annualcosts

Energy costper kWh

Annual energyproduction

Annual operatingand

maintenance costs

kwh

kwh

Figure 9 Cost structure per kWh for wind power projects

Table 6 Turbine establishment cost [20ndash26]

Establishment parameters Cost (euro)Turbine and installation 9000000Project preparation costs (permission and licences) 650000Land rental cost 100000Network connections (cable transformer andcommunication) 1800000

Foundation and installation (foundation roadand site) 1200000

Finance (consultancy insurance and bank) 450000Total 13200000

Table 7 Annual income-expense table [20ndash26]

euroyearElectricity sales income 2109360Emission sales income 82608Annual operational income (euroyear) 2191168Rental amount 100000Maintenance-repair insurance 130000Labour 30000Annual licence cost 1000General administrative costs 5000Annual operational expenses (euroyear) 266000Annual operational margin (euroyear) 1925168

Advances in Meteorology 7

systems have been considered as mentioned in the report ofthe European Wind Power Union and land rental con-sultancy maintenance and repair and yearly licence costshave been included into the analysis as permanently paidcosts within years (Table 6)

Annual 38352GWh power generation has been calcu-lated for the wind power plant formed with six 2MWVESTAS V80 wind turbines In this case annual income hasbeen calculated as 2109360 euro with 55 eurocentkWh electricitysales price Emission sales income for the 38352GWh powergeneration has been calculated as 82608 euro annually Asa result of the calculations made annual income and ex-penses as well as the annual margin are shown in Table 7

In this study operational expenses maintenance repairinsurance rental and licence costs and some valueschanging as per year such as interest have been re ected asaverage values in the income-expense table by taking intoconsideration the project lifetime In case of selling theelectricity at 55 eurocentkWh at the wind power plant formedby 6 VESTAS V80 wind turbines it is seen that the cash owwill pass to the positive state at 7 years and the projectreturns at year 7

In this study cost per kWh for the capacity factor ob-tained from selected turbines has been calculated By usingthe capacity factor as 3648 calculated for the VESTAS V80turbine used in the established system the annual generationamount of the plant in kWh has been found

In order to calculate the unit cost of electricity powerobtained from wind turbines it is necessary to know theregaining factor of investment e regaining factor ofcapital (C) is calculated with the following formula

C i(1 + i)n

(1 + i)n minus 1 (15)

where i is the interest rate () and n is the turbine lifetime(years)

Generation cost is as follows

euroU CT(C + I)

E (16)

where euroU is the generation cost (eurokWh) I is the servicemaintenance and insurance (operational) expenses () CTis the total establishment cost of the wind turbine (euro) and Eis the annual generated power amount (kWh)

e power to be generated by the wind turbine(kWhyear) in the region (E) where the establishment of thewind turbine is planned is dened with the followingformula

E ηkay middot Cp middot12middot ρh middot S middotsum

ki1Δti middot U3

r (17)

where Δti is the time interval (h) U is the wind speed (ms)ρh is the air density (kgm

3) r is the radius S is the scannedarea by the wind turbine (m2) Cp is the power coecientηkay is the fraction losses at bearings (eg 0996) losses at thegear box (eg 0972) and losses at the electricity generator(eg 094) and general total coecient (may be consideredas approximately ηkay 09 at calculations)

C 0075(1 + 0075)25

(1 + 0075)25 minus 1 008971

U 13200000(008971 + 002)

33955200 00426 euroKWh

426 eurocKWh

(18)

In the calculations made unit cost values to be used asthe input unit in the fuzzy logic system are found as426 eurockWh

71

309

3648

2

426

7Amortization

period

kWhcost

Unpredictableexpense

Capacityfactor

Averagepower density

Averagewind speed

Fuzzy logiccontroller

Fuzzy logiccontroller 2

Display 1

Scope 1

Scope

Display

Display 2

Scope 2

Fuzzy logiccontroller 1

847256

835742

775063

Figure 10 Display of the result values generated by the fuzzy-based decision-making system designed according to the input valuescalculated by the WAsP

8 Advances in Meteorology

5 Conclusion

In this study average wind speed and wind direction data ofArapgir province measured at 10m height and 10-minuteintervals and taken from OMGI (Automatic MeteorologicalObservation Station) were analyzed by WAsP software Inthe analysis made the average wind speed is found as275ms the average power density is found as 53Wm2 thefigure parameter is found as 151 and the scale parameter isfound as 42ms obtained at 10m according to the Weibulldistribution

As a result of the calculations made in WAsP 12 sectorshave been used in the wind mill and dominant wind di-rection has been observed as SE at 120deg sector e change ofthe Hellmann coefficient as per the direction by usingexisting data in addition to the analysis made as per the windblowing has been examined and the average Hellmanncoefficient has been calculated as 034

As a result of this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are usedwith the wind power plant to be established in the region ithas been calculated that annual gross 41316GWh and net38352GWh electricity shall be generated It has been un-derstood that 717 loss shall occur as a result of the in-terference of wind turbines with each other

In case six 2MW turbines operate under nominal powerduring 8760 hours a maximum of 105120GWh energy maybe generated As a result of the calculations made in WAsPit has been determined that the wind power plant formed bysix 2MW turbines shall generate 38352GWh energy in oneyear and the capacity factor shall be realized as 3648

Annual generation values of the plant have been found as426 eurockWh by using 3648 capacity factor calculated forthe VESTAS V80 turbine used in the established systemeamortization period is calculated as 7 years Unpredictableexpenditure was selected as 2 of the initial installation coste height of the tower of the wind turbine was determinedas 80m e wind speed at 80m height was calculated as71ms e power density at the height of 80m was cal-culated as 309 In Figure 10 the final relevance factor ofrunning the system in the MatlabSimulink model is 8357

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors certify that there are no actual or potentialconflicts of interest in relation to this article

References

[1] October 2017 httpswwwmgmgovtrgenelruzgar-atlasiaspx[2] A Bechkaoui A Ameur S Bouras and A Hadjadj ldquoOpen-

circuit and inter-turn short-circuit detection in PMSG forwind turbine applications using fuzzy logicrdquo Energy Procediavol 74 pp 1323ndash1336 2015

[3] E T Karagol and I KavazDunyada ve Turkiyersquode YenilenebilirEnerji Turkuvaz Haberlesme ve Yayıncılık AS Vol 197Istanbul Turkey 2017

[4] E Kovac-Andriḉ T Radanoviḉ I Topaloviḉ B Markoviḉand N Sakaḉ ldquoTemporal variations in concentrations ofozone nitrogen dioxide and carbon monoxide at OsijekCroatiardquo Advances inMeteorology vol 2013 Article ID 4697867 pages 2013

[5] J L Pearce J Beringer N Nicholls R J Hyndman andN J Tapper ldquoQuantifying the influence of local meteorologyon air quality using generalized additive modelsrdquoAtmosphericEnvironment vol 45 no 6 pp 1328ndash1336 2011

[6] S Mathew Wind Energy Fundamentals Resource Analysisand Economics Springer Berlin Germany 2006

[7] S Mentes Ruzgar Enerjisi ve Sistemleri Ders Notları ITUUccedilak Uzay Fakultesi Istanbul Turkey 2008

[8] P Gipe Wind Power Chelsea Greer Publishing CompanyWhite River Junction VT USA 2003

[9] H Badihi and Y Zhang ldquoPassive fault-tolerant cooperativecontrol in an offshore wind farmrdquo Energy Procedia vol 105pp 2959ndash2964 2017

[10] November 2017 httpsdaskuzawordpresscomarapgir-cografyasi-1

[11] November 2017 httpwwwarapgirgovtrcografi-durumu[12] J Kim and B Yum ldquoSelection between Weibull and log-

normal distributions a comparative simulation studyrdquoComputational Statistics and Data Analysis vol 53 no 2pp 477ndash485 2008

[13] J F Manwell J G Morgan and A L Rogers Wind EnergyExplained 3eory Design and Application John Wiley andSons Ltd West Sussex UK 2002

[14] S Rehman andNM Al-Abbadi ldquoWind shear coefficients andtheir effect on energy productionrdquo Energy Conversion andManagement vol 46 no 15-16 pp 2578ndash2591 2005

[15] H Lettau ldquoNote on aerodynamic roughness-parameter es-timation on the basis of roughness-element distributionrdquoJournal of Applied Meteorology vol 8 no 5 pp 828ndash8321969

[16] E K Akpinar and S Akpinar ldquoAn assessment on seasonalanalysis of wind energy characteristics and wind turbinecharacteristicsrdquo Energy Conversion and Management vol 46no 11-12 pp 1848ndash1867 2005

[17] A Senkal and N S Ccediletin Turkiyersquode kurulu olan buyuk guccedilluruzgar santrallerinin kapasite faktorlerine genel bir bakıs EgeBolgesi Enerji Forumu Denizli Turkey 2009

[18] F Kose andM Ozgoren ldquoRuzgar Enerjisi Potansiyeli Olccedilumuve Ruzgar Turbini seccedilimirdquo Muhendis ve Makine vol 551pp 20ndash30 2005

[19] S Krohn P E Morthorst and S Awerbuch3e Economics ofWind Energy 3e European Wind Energy Association ReportEuropean Wind Energy Association Brussels Belgium 2009

[20] P Nielsen Offshore Wind Energy Projects Feasibility StudyGuidelines SEAWIND-Altener project 41030Z01-2001Ver 30 EMD International AS Aalborg Denmark 2003

[21] C Camporeale Offshore Wind Farms in Italy 3e Sicily CaseOwemes Citavecchia Italy 2006

[22] ODE Study of the Costs of Offshore Wind GenerationmdashAReport to the Renewables Advisory Board (RAB) amp DTI URNNUMBER 07779 Government publication 114 pagesLondon UK 2007

[23] EWEA Financing and Investment Trends 2016 Vol 9 EWEABrussels Belgium May 2017

Advances in Meteorology 9

turbines in technical terms In Figure 8 the micrositingstudy has been shown where six 2MW VESTAS V80 windturbines are used

Following this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are

used electricity generation of annual gross 41316 GWhand net 38352 GWh has been calculated As a resultof the interference of turbines with each other it hasbeen understood that there will be a 717 loss Annualgeneration values have been shown in Table 3 in themicrositing study where 6 VESTAS V80 wind turbinesare used

In Table 4 generation and loss values for each turbine aregiven for the micrositing study where 6 V80 wind turbineshave been used

As can be shown in Table 4 when annual net powergeneration values are being considered the maximum gen-eration amount 7082GWh was at turbine number 4 and theminimum generation amount 5713GWh was at turbine 6During the micrositing study it has been calculated that themaximumwake loss 979was at turbine 6 and theminimumwake loss 534 was at turbine number 4

45 Capacity Factor of the Area e capacity factor whichis an important parameter and has to be known both bythe generators and consumers is the division of energygenerated in a specific time frame to the maximumenergy that can be generated at that specific time frame[16 17]

In this study the capacity factor has been calculated forthe turbine used during micrositing and turbine selectionstudies and generated micrositings

CF ET

TlowastPR (14)

where CF is the capacity factor ET is the generated totalpower PR is the nominal power value and T is the time

In the micrositing study the annual calculated genera-tion amount for six 2MW V80 wind turbines maximumamount that can be generated from turbines and capacityfactor are shown in Table 5

In case of operation under nominal power of six 2MWturbines during 8760 hours in one year they may generatemaximum 8760times12105120GWh power in one year As

Figure 7 Power density map of the area

Figure 8 Micrositing process of turbines

Table 3 Annual generation values of 6 V80 wind turbines

Parameter Total Min MaxNet annual generation (GWh) 38352 5713 7082Gross annual generation (GWh) 41316 6034 7897Wake (track) loss () 7173 mdash mdash

Figure 6 Average wind severity map calculated in the area

Table 4 Annual generation and loss values of 6 V80 wind turbines

Turbinenumber

Hubheight (m)

Net annualgeneration (GWh)

Wake(track) loss

Turbine 1 80 6789 709Turbine 2 80 6145 618Turbine 3 80 5824 962Turbine 4 80 7082 534Turbine 5 80 6799 829Turbine 6 80 5713 979

6 Advances in Meteorology

a result of the calculations made in WAsP it has beendetermined that the wind power generated by the windpower plant formed by six 2MW turbines shall generate38352GWh power in one year and the capacity factor shallbe realized as 38352105120 03648 in other words3648 e capacity factor which is calculated at and above25 for the wind turbines deemed appropriate for the in-vestment in Turkey [18]

46 Economical Analysis of the Data One of the most im-portant studies that have to be carried out while establishinga wind turbine to a region is the calculation of kWh powercost Generally the cost of one wind power project per kWhis found by proportioning the annual total cost to the annualpower generation amount

e annual power generation amount changesdepending on the parameters such as the hub height ofturbine rotor diameter average wind severity of the areaand annual cost may be correlated with turbine priceturbine foundations inner site road construction costsinvestment costs and project lifetime In Figure 9 the coststructure is shown per kWh for wind power projects [19]

In this study within the rst establishment turbine costnetwork connection foundation and establishment costselectricity installation and road construction and control

Table 5 Capacity factor of the area

Turbinemodel

Turbinenumber

Installedpower

Annual calculated production amount(GWh)

Nominal power to be produced(GWh)

Capacity factor()

VESTAS V80 6 12 38352 10512 3648

Turbineand

installation

Project lifeCapital cost

pa

Turbine price inturbine-based roads

Rotor diameterhub height Average wind speed

region properties

Annual capitalcosts

Total annualcosts

Energy costper kWh

Annual energyproduction

Annual operatingand

maintenance costs

kwh

kwh

Figure 9 Cost structure per kWh for wind power projects

Table 6 Turbine establishment cost [20ndash26]

Establishment parameters Cost (euro)Turbine and installation 9000000Project preparation costs (permission and licences) 650000Land rental cost 100000Network connections (cable transformer andcommunication) 1800000

Foundation and installation (foundation roadand site) 1200000

Finance (consultancy insurance and bank) 450000Total 13200000

Table 7 Annual income-expense table [20ndash26]

euroyearElectricity sales income 2109360Emission sales income 82608Annual operational income (euroyear) 2191168Rental amount 100000Maintenance-repair insurance 130000Labour 30000Annual licence cost 1000General administrative costs 5000Annual operational expenses (euroyear) 266000Annual operational margin (euroyear) 1925168

Advances in Meteorology 7

systems have been considered as mentioned in the report ofthe European Wind Power Union and land rental con-sultancy maintenance and repair and yearly licence costshave been included into the analysis as permanently paidcosts within years (Table 6)

Annual 38352GWh power generation has been calcu-lated for the wind power plant formed with six 2MWVESTAS V80 wind turbines In this case annual income hasbeen calculated as 2109360 euro with 55 eurocentkWh electricitysales price Emission sales income for the 38352GWh powergeneration has been calculated as 82608 euro annually Asa result of the calculations made annual income and ex-penses as well as the annual margin are shown in Table 7

In this study operational expenses maintenance repairinsurance rental and licence costs and some valueschanging as per year such as interest have been re ected asaverage values in the income-expense table by taking intoconsideration the project lifetime In case of selling theelectricity at 55 eurocentkWh at the wind power plant formedby 6 VESTAS V80 wind turbines it is seen that the cash owwill pass to the positive state at 7 years and the projectreturns at year 7

In this study cost per kWh for the capacity factor ob-tained from selected turbines has been calculated By usingthe capacity factor as 3648 calculated for the VESTAS V80turbine used in the established system the annual generationamount of the plant in kWh has been found

In order to calculate the unit cost of electricity powerobtained from wind turbines it is necessary to know theregaining factor of investment e regaining factor ofcapital (C) is calculated with the following formula

C i(1 + i)n

(1 + i)n minus 1 (15)

where i is the interest rate () and n is the turbine lifetime(years)

Generation cost is as follows

euroU CT(C + I)

E (16)

where euroU is the generation cost (eurokWh) I is the servicemaintenance and insurance (operational) expenses () CTis the total establishment cost of the wind turbine (euro) and Eis the annual generated power amount (kWh)

e power to be generated by the wind turbine(kWhyear) in the region (E) where the establishment of thewind turbine is planned is dened with the followingformula

E ηkay middot Cp middot12middot ρh middot S middotsum

ki1Δti middot U3

r (17)

where Δti is the time interval (h) U is the wind speed (ms)ρh is the air density (kgm

3) r is the radius S is the scannedarea by the wind turbine (m2) Cp is the power coecientηkay is the fraction losses at bearings (eg 0996) losses at thegear box (eg 0972) and losses at the electricity generator(eg 094) and general total coecient (may be consideredas approximately ηkay 09 at calculations)

C 0075(1 + 0075)25

(1 + 0075)25 minus 1 008971

U 13200000(008971 + 002)

33955200 00426 euroKWh

426 eurocKWh

(18)

In the calculations made unit cost values to be used asthe input unit in the fuzzy logic system are found as426 eurockWh

71

309

3648

2

426

7Amortization

period

kWhcost

Unpredictableexpense

Capacityfactor

Averagepower density

Averagewind speed

Fuzzy logiccontroller

Fuzzy logiccontroller 2

Display 1

Scope 1

Scope

Display

Display 2

Scope 2

Fuzzy logiccontroller 1

847256

835742

775063

Figure 10 Display of the result values generated by the fuzzy-based decision-making system designed according to the input valuescalculated by the WAsP

8 Advances in Meteorology

5 Conclusion

In this study average wind speed and wind direction data ofArapgir province measured at 10m height and 10-minuteintervals and taken from OMGI (Automatic MeteorologicalObservation Station) were analyzed by WAsP software Inthe analysis made the average wind speed is found as275ms the average power density is found as 53Wm2 thefigure parameter is found as 151 and the scale parameter isfound as 42ms obtained at 10m according to the Weibulldistribution

As a result of the calculations made in WAsP 12 sectorshave been used in the wind mill and dominant wind di-rection has been observed as SE at 120deg sector e change ofthe Hellmann coefficient as per the direction by usingexisting data in addition to the analysis made as per the windblowing has been examined and the average Hellmanncoefficient has been calculated as 034

As a result of this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are usedwith the wind power plant to be established in the region ithas been calculated that annual gross 41316GWh and net38352GWh electricity shall be generated It has been un-derstood that 717 loss shall occur as a result of the in-terference of wind turbines with each other

In case six 2MW turbines operate under nominal powerduring 8760 hours a maximum of 105120GWh energy maybe generated As a result of the calculations made in WAsPit has been determined that the wind power plant formed bysix 2MW turbines shall generate 38352GWh energy in oneyear and the capacity factor shall be realized as 3648

Annual generation values of the plant have been found as426 eurockWh by using 3648 capacity factor calculated forthe VESTAS V80 turbine used in the established systemeamortization period is calculated as 7 years Unpredictableexpenditure was selected as 2 of the initial installation coste height of the tower of the wind turbine was determinedas 80m e wind speed at 80m height was calculated as71ms e power density at the height of 80m was cal-culated as 309 In Figure 10 the final relevance factor ofrunning the system in the MatlabSimulink model is 8357

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors certify that there are no actual or potentialconflicts of interest in relation to this article

References

[1] October 2017 httpswwwmgmgovtrgenelruzgar-atlasiaspx[2] A Bechkaoui A Ameur S Bouras and A Hadjadj ldquoOpen-

circuit and inter-turn short-circuit detection in PMSG forwind turbine applications using fuzzy logicrdquo Energy Procediavol 74 pp 1323ndash1336 2015

[3] E T Karagol and I KavazDunyada ve Turkiyersquode YenilenebilirEnerji Turkuvaz Haberlesme ve Yayıncılık AS Vol 197Istanbul Turkey 2017

[4] E Kovac-Andriḉ T Radanoviḉ I Topaloviḉ B Markoviḉand N Sakaḉ ldquoTemporal variations in concentrations ofozone nitrogen dioxide and carbon monoxide at OsijekCroatiardquo Advances inMeteorology vol 2013 Article ID 4697867 pages 2013

[5] J L Pearce J Beringer N Nicholls R J Hyndman andN J Tapper ldquoQuantifying the influence of local meteorologyon air quality using generalized additive modelsrdquoAtmosphericEnvironment vol 45 no 6 pp 1328ndash1336 2011

[6] S Mathew Wind Energy Fundamentals Resource Analysisand Economics Springer Berlin Germany 2006

[7] S Mentes Ruzgar Enerjisi ve Sistemleri Ders Notları ITUUccedilak Uzay Fakultesi Istanbul Turkey 2008

[8] P Gipe Wind Power Chelsea Greer Publishing CompanyWhite River Junction VT USA 2003

[9] H Badihi and Y Zhang ldquoPassive fault-tolerant cooperativecontrol in an offshore wind farmrdquo Energy Procedia vol 105pp 2959ndash2964 2017

[10] November 2017 httpsdaskuzawordpresscomarapgir-cografyasi-1

[11] November 2017 httpwwwarapgirgovtrcografi-durumu[12] J Kim and B Yum ldquoSelection between Weibull and log-

normal distributions a comparative simulation studyrdquoComputational Statistics and Data Analysis vol 53 no 2pp 477ndash485 2008

[13] J F Manwell J G Morgan and A L Rogers Wind EnergyExplained 3eory Design and Application John Wiley andSons Ltd West Sussex UK 2002

[14] S Rehman andNM Al-Abbadi ldquoWind shear coefficients andtheir effect on energy productionrdquo Energy Conversion andManagement vol 46 no 15-16 pp 2578ndash2591 2005

[15] H Lettau ldquoNote on aerodynamic roughness-parameter es-timation on the basis of roughness-element distributionrdquoJournal of Applied Meteorology vol 8 no 5 pp 828ndash8321969

[16] E K Akpinar and S Akpinar ldquoAn assessment on seasonalanalysis of wind energy characteristics and wind turbinecharacteristicsrdquo Energy Conversion and Management vol 46no 11-12 pp 1848ndash1867 2005

[17] A Senkal and N S Ccediletin Turkiyersquode kurulu olan buyuk guccedilluruzgar santrallerinin kapasite faktorlerine genel bir bakıs EgeBolgesi Enerji Forumu Denizli Turkey 2009

[18] F Kose andM Ozgoren ldquoRuzgar Enerjisi Potansiyeli Olccedilumuve Ruzgar Turbini seccedilimirdquo Muhendis ve Makine vol 551pp 20ndash30 2005

[19] S Krohn P E Morthorst and S Awerbuch3e Economics ofWind Energy 3e European Wind Energy Association ReportEuropean Wind Energy Association Brussels Belgium 2009

[20] P Nielsen Offshore Wind Energy Projects Feasibility StudyGuidelines SEAWIND-Altener project 41030Z01-2001Ver 30 EMD International AS Aalborg Denmark 2003

[21] C Camporeale Offshore Wind Farms in Italy 3e Sicily CaseOwemes Citavecchia Italy 2006

[22] ODE Study of the Costs of Offshore Wind GenerationmdashAReport to the Renewables Advisory Board (RAB) amp DTI URNNUMBER 07779 Government publication 114 pagesLondon UK 2007

[23] EWEA Financing and Investment Trends 2016 Vol 9 EWEABrussels Belgium May 2017

Advances in Meteorology 9

a result of the calculations made in WAsP it has beendetermined that the wind power generated by the windpower plant formed by six 2MW turbines shall generate38352GWh power in one year and the capacity factor shallbe realized as 38352105120 03648 in other words3648 e capacity factor which is calculated at and above25 for the wind turbines deemed appropriate for the in-vestment in Turkey [18]

46 Economical Analysis of the Data One of the most im-portant studies that have to be carried out while establishinga wind turbine to a region is the calculation of kWh powercost Generally the cost of one wind power project per kWhis found by proportioning the annual total cost to the annualpower generation amount

e annual power generation amount changesdepending on the parameters such as the hub height ofturbine rotor diameter average wind severity of the areaand annual cost may be correlated with turbine priceturbine foundations inner site road construction costsinvestment costs and project lifetime In Figure 9 the coststructure is shown per kWh for wind power projects [19]

In this study within the rst establishment turbine costnetwork connection foundation and establishment costselectricity installation and road construction and control

Table 5 Capacity factor of the area

Turbinemodel

Turbinenumber

Installedpower

Annual calculated production amount(GWh)

Nominal power to be produced(GWh)

Capacity factor()

VESTAS V80 6 12 38352 10512 3648

Turbineand

installation

Project lifeCapital cost

pa

Turbine price inturbine-based roads

Rotor diameterhub height Average wind speed

region properties

Annual capitalcosts

Total annualcosts

Energy costper kWh

Annual energyproduction

Annual operatingand

maintenance costs

kwh

kwh

Figure 9 Cost structure per kWh for wind power projects

Table 6 Turbine establishment cost [20ndash26]

Establishment parameters Cost (euro)Turbine and installation 9000000Project preparation costs (permission and licences) 650000Land rental cost 100000Network connections (cable transformer andcommunication) 1800000

Foundation and installation (foundation roadand site) 1200000

Finance (consultancy insurance and bank) 450000Total 13200000

Table 7 Annual income-expense table [20ndash26]

euroyearElectricity sales income 2109360Emission sales income 82608Annual operational income (euroyear) 2191168Rental amount 100000Maintenance-repair insurance 130000Labour 30000Annual licence cost 1000General administrative costs 5000Annual operational expenses (euroyear) 266000Annual operational margin (euroyear) 1925168

Advances in Meteorology 7

systems have been considered as mentioned in the report ofthe European Wind Power Union and land rental con-sultancy maintenance and repair and yearly licence costshave been included into the analysis as permanently paidcosts within years (Table 6)

Annual 38352GWh power generation has been calcu-lated for the wind power plant formed with six 2MWVESTAS V80 wind turbines In this case annual income hasbeen calculated as 2109360 euro with 55 eurocentkWh electricitysales price Emission sales income for the 38352GWh powergeneration has been calculated as 82608 euro annually Asa result of the calculations made annual income and ex-penses as well as the annual margin are shown in Table 7

In this study operational expenses maintenance repairinsurance rental and licence costs and some valueschanging as per year such as interest have been re ected asaverage values in the income-expense table by taking intoconsideration the project lifetime In case of selling theelectricity at 55 eurocentkWh at the wind power plant formedby 6 VESTAS V80 wind turbines it is seen that the cash owwill pass to the positive state at 7 years and the projectreturns at year 7

In this study cost per kWh for the capacity factor ob-tained from selected turbines has been calculated By usingthe capacity factor as 3648 calculated for the VESTAS V80turbine used in the established system the annual generationamount of the plant in kWh has been found

In order to calculate the unit cost of electricity powerobtained from wind turbines it is necessary to know theregaining factor of investment e regaining factor ofcapital (C) is calculated with the following formula

C i(1 + i)n

(1 + i)n minus 1 (15)

where i is the interest rate () and n is the turbine lifetime(years)

Generation cost is as follows

euroU CT(C + I)

E (16)

where euroU is the generation cost (eurokWh) I is the servicemaintenance and insurance (operational) expenses () CTis the total establishment cost of the wind turbine (euro) and Eis the annual generated power amount (kWh)

e power to be generated by the wind turbine(kWhyear) in the region (E) where the establishment of thewind turbine is planned is dened with the followingformula

E ηkay middot Cp middot12middot ρh middot S middotsum

ki1Δti middot U3

r (17)

where Δti is the time interval (h) U is the wind speed (ms)ρh is the air density (kgm

3) r is the radius S is the scannedarea by the wind turbine (m2) Cp is the power coecientηkay is the fraction losses at bearings (eg 0996) losses at thegear box (eg 0972) and losses at the electricity generator(eg 094) and general total coecient (may be consideredas approximately ηkay 09 at calculations)

C 0075(1 + 0075)25

(1 + 0075)25 minus 1 008971

U 13200000(008971 + 002)

33955200 00426 euroKWh

426 eurocKWh

(18)

In the calculations made unit cost values to be used asthe input unit in the fuzzy logic system are found as426 eurockWh

71

309

3648

2

426

7Amortization

period

kWhcost

Unpredictableexpense

Capacityfactor

Averagepower density

Averagewind speed

Fuzzy logiccontroller

Fuzzy logiccontroller 2

Display 1

Scope 1

Scope

Display

Display 2

Scope 2

Fuzzy logiccontroller 1

847256

835742

775063

Figure 10 Display of the result values generated by the fuzzy-based decision-making system designed according to the input valuescalculated by the WAsP

8 Advances in Meteorology

5 Conclusion

In this study average wind speed and wind direction data ofArapgir province measured at 10m height and 10-minuteintervals and taken from OMGI (Automatic MeteorologicalObservation Station) were analyzed by WAsP software Inthe analysis made the average wind speed is found as275ms the average power density is found as 53Wm2 thefigure parameter is found as 151 and the scale parameter isfound as 42ms obtained at 10m according to the Weibulldistribution

As a result of the calculations made in WAsP 12 sectorshave been used in the wind mill and dominant wind di-rection has been observed as SE at 120deg sector e change ofthe Hellmann coefficient as per the direction by usingexisting data in addition to the analysis made as per the windblowing has been examined and the average Hellmanncoefficient has been calculated as 034

As a result of this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are usedwith the wind power plant to be established in the region ithas been calculated that annual gross 41316GWh and net38352GWh electricity shall be generated It has been un-derstood that 717 loss shall occur as a result of the in-terference of wind turbines with each other

In case six 2MW turbines operate under nominal powerduring 8760 hours a maximum of 105120GWh energy maybe generated As a result of the calculations made in WAsPit has been determined that the wind power plant formed bysix 2MW turbines shall generate 38352GWh energy in oneyear and the capacity factor shall be realized as 3648

Annual generation values of the plant have been found as426 eurockWh by using 3648 capacity factor calculated forthe VESTAS V80 turbine used in the established systemeamortization period is calculated as 7 years Unpredictableexpenditure was selected as 2 of the initial installation coste height of the tower of the wind turbine was determinedas 80m e wind speed at 80m height was calculated as71ms e power density at the height of 80m was cal-culated as 309 In Figure 10 the final relevance factor ofrunning the system in the MatlabSimulink model is 8357

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors certify that there are no actual or potentialconflicts of interest in relation to this article

References

[1] October 2017 httpswwwmgmgovtrgenelruzgar-atlasiaspx[2] A Bechkaoui A Ameur S Bouras and A Hadjadj ldquoOpen-

circuit and inter-turn short-circuit detection in PMSG forwind turbine applications using fuzzy logicrdquo Energy Procediavol 74 pp 1323ndash1336 2015

[3] E T Karagol and I KavazDunyada ve Turkiyersquode YenilenebilirEnerji Turkuvaz Haberlesme ve Yayıncılık AS Vol 197Istanbul Turkey 2017

[4] E Kovac-Andriḉ T Radanoviḉ I Topaloviḉ B Markoviḉand N Sakaḉ ldquoTemporal variations in concentrations ofozone nitrogen dioxide and carbon monoxide at OsijekCroatiardquo Advances inMeteorology vol 2013 Article ID 4697867 pages 2013

[5] J L Pearce J Beringer N Nicholls R J Hyndman andN J Tapper ldquoQuantifying the influence of local meteorologyon air quality using generalized additive modelsrdquoAtmosphericEnvironment vol 45 no 6 pp 1328ndash1336 2011

[6] S Mathew Wind Energy Fundamentals Resource Analysisand Economics Springer Berlin Germany 2006

[7] S Mentes Ruzgar Enerjisi ve Sistemleri Ders Notları ITUUccedilak Uzay Fakultesi Istanbul Turkey 2008

[8] P Gipe Wind Power Chelsea Greer Publishing CompanyWhite River Junction VT USA 2003

[9] H Badihi and Y Zhang ldquoPassive fault-tolerant cooperativecontrol in an offshore wind farmrdquo Energy Procedia vol 105pp 2959ndash2964 2017

[10] November 2017 httpsdaskuzawordpresscomarapgir-cografyasi-1

[11] November 2017 httpwwwarapgirgovtrcografi-durumu[12] J Kim and B Yum ldquoSelection between Weibull and log-

normal distributions a comparative simulation studyrdquoComputational Statistics and Data Analysis vol 53 no 2pp 477ndash485 2008

[13] J F Manwell J G Morgan and A L Rogers Wind EnergyExplained 3eory Design and Application John Wiley andSons Ltd West Sussex UK 2002

[14] S Rehman andNM Al-Abbadi ldquoWind shear coefficients andtheir effect on energy productionrdquo Energy Conversion andManagement vol 46 no 15-16 pp 2578ndash2591 2005

[15] H Lettau ldquoNote on aerodynamic roughness-parameter es-timation on the basis of roughness-element distributionrdquoJournal of Applied Meteorology vol 8 no 5 pp 828ndash8321969

[16] E K Akpinar and S Akpinar ldquoAn assessment on seasonalanalysis of wind energy characteristics and wind turbinecharacteristicsrdquo Energy Conversion and Management vol 46no 11-12 pp 1848ndash1867 2005

[17] A Senkal and N S Ccediletin Turkiyersquode kurulu olan buyuk guccedilluruzgar santrallerinin kapasite faktorlerine genel bir bakıs EgeBolgesi Enerji Forumu Denizli Turkey 2009

[18] F Kose andM Ozgoren ldquoRuzgar Enerjisi Potansiyeli Olccedilumuve Ruzgar Turbini seccedilimirdquo Muhendis ve Makine vol 551pp 20ndash30 2005

[19] S Krohn P E Morthorst and S Awerbuch3e Economics ofWind Energy 3e European Wind Energy Association ReportEuropean Wind Energy Association Brussels Belgium 2009

[20] P Nielsen Offshore Wind Energy Projects Feasibility StudyGuidelines SEAWIND-Altener project 41030Z01-2001Ver 30 EMD International AS Aalborg Denmark 2003

[21] C Camporeale Offshore Wind Farms in Italy 3e Sicily CaseOwemes Citavecchia Italy 2006

[22] ODE Study of the Costs of Offshore Wind GenerationmdashAReport to the Renewables Advisory Board (RAB) amp DTI URNNUMBER 07779 Government publication 114 pagesLondon UK 2007

[23] EWEA Financing and Investment Trends 2016 Vol 9 EWEABrussels Belgium May 2017

Advances in Meteorology 9

systems have been considered as mentioned in the report ofthe European Wind Power Union and land rental con-sultancy maintenance and repair and yearly licence costshave been included into the analysis as permanently paidcosts within years (Table 6)

Annual 38352GWh power generation has been calcu-lated for the wind power plant formed with six 2MWVESTAS V80 wind turbines In this case annual income hasbeen calculated as 2109360 euro with 55 eurocentkWh electricitysales price Emission sales income for the 38352GWh powergeneration has been calculated as 82608 euro annually Asa result of the calculations made annual income and ex-penses as well as the annual margin are shown in Table 7

In this study operational expenses maintenance repairinsurance rental and licence costs and some valueschanging as per year such as interest have been re ected asaverage values in the income-expense table by taking intoconsideration the project lifetime In case of selling theelectricity at 55 eurocentkWh at the wind power plant formedby 6 VESTAS V80 wind turbines it is seen that the cash owwill pass to the positive state at 7 years and the projectreturns at year 7

In this study cost per kWh for the capacity factor ob-tained from selected turbines has been calculated By usingthe capacity factor as 3648 calculated for the VESTAS V80turbine used in the established system the annual generationamount of the plant in kWh has been found

In order to calculate the unit cost of electricity powerobtained from wind turbines it is necessary to know theregaining factor of investment e regaining factor ofcapital (C) is calculated with the following formula

C i(1 + i)n

(1 + i)n minus 1 (15)

where i is the interest rate () and n is the turbine lifetime(years)

Generation cost is as follows

euroU CT(C + I)

E (16)

where euroU is the generation cost (eurokWh) I is the servicemaintenance and insurance (operational) expenses () CTis the total establishment cost of the wind turbine (euro) and Eis the annual generated power amount (kWh)

e power to be generated by the wind turbine(kWhyear) in the region (E) where the establishment of thewind turbine is planned is dened with the followingformula

E ηkay middot Cp middot12middot ρh middot S middotsum

ki1Δti middot U3

r (17)

where Δti is the time interval (h) U is the wind speed (ms)ρh is the air density (kgm

3) r is the radius S is the scannedarea by the wind turbine (m2) Cp is the power coecientηkay is the fraction losses at bearings (eg 0996) losses at thegear box (eg 0972) and losses at the electricity generator(eg 094) and general total coecient (may be consideredas approximately ηkay 09 at calculations)

C 0075(1 + 0075)25

(1 + 0075)25 minus 1 008971

U 13200000(008971 + 002)

33955200 00426 euroKWh

426 eurocKWh

(18)

In the calculations made unit cost values to be used asthe input unit in the fuzzy logic system are found as426 eurockWh

71

309

3648

2

426

7Amortization

period

kWhcost

Unpredictableexpense

Capacityfactor

Averagepower density

Averagewind speed

Fuzzy logiccontroller

Fuzzy logiccontroller 2

Display 1

Scope 1

Scope

Display

Display 2

Scope 2

Fuzzy logiccontroller 1

847256

835742

775063

Figure 10 Display of the result values generated by the fuzzy-based decision-making system designed according to the input valuescalculated by the WAsP

8 Advances in Meteorology

5 Conclusion

In this study average wind speed and wind direction data ofArapgir province measured at 10m height and 10-minuteintervals and taken from OMGI (Automatic MeteorologicalObservation Station) were analyzed by WAsP software Inthe analysis made the average wind speed is found as275ms the average power density is found as 53Wm2 thefigure parameter is found as 151 and the scale parameter isfound as 42ms obtained at 10m according to the Weibulldistribution

As a result of the calculations made in WAsP 12 sectorshave been used in the wind mill and dominant wind di-rection has been observed as SE at 120deg sector e change ofthe Hellmann coefficient as per the direction by usingexisting data in addition to the analysis made as per the windblowing has been examined and the average Hellmanncoefficient has been calculated as 034

As a result of this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are usedwith the wind power plant to be established in the region ithas been calculated that annual gross 41316GWh and net38352GWh electricity shall be generated It has been un-derstood that 717 loss shall occur as a result of the in-terference of wind turbines with each other

In case six 2MW turbines operate under nominal powerduring 8760 hours a maximum of 105120GWh energy maybe generated As a result of the calculations made in WAsPit has been determined that the wind power plant formed bysix 2MW turbines shall generate 38352GWh energy in oneyear and the capacity factor shall be realized as 3648

Annual generation values of the plant have been found as426 eurockWh by using 3648 capacity factor calculated forthe VESTAS V80 turbine used in the established systemeamortization period is calculated as 7 years Unpredictableexpenditure was selected as 2 of the initial installation coste height of the tower of the wind turbine was determinedas 80m e wind speed at 80m height was calculated as71ms e power density at the height of 80m was cal-culated as 309 In Figure 10 the final relevance factor ofrunning the system in the MatlabSimulink model is 8357

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors certify that there are no actual or potentialconflicts of interest in relation to this article

References

[1] October 2017 httpswwwmgmgovtrgenelruzgar-atlasiaspx[2] A Bechkaoui A Ameur S Bouras and A Hadjadj ldquoOpen-

circuit and inter-turn short-circuit detection in PMSG forwind turbine applications using fuzzy logicrdquo Energy Procediavol 74 pp 1323ndash1336 2015

[3] E T Karagol and I KavazDunyada ve Turkiyersquode YenilenebilirEnerji Turkuvaz Haberlesme ve Yayıncılık AS Vol 197Istanbul Turkey 2017

[4] E Kovac-Andriḉ T Radanoviḉ I Topaloviḉ B Markoviḉand N Sakaḉ ldquoTemporal variations in concentrations ofozone nitrogen dioxide and carbon monoxide at OsijekCroatiardquo Advances inMeteorology vol 2013 Article ID 4697867 pages 2013

[5] J L Pearce J Beringer N Nicholls R J Hyndman andN J Tapper ldquoQuantifying the influence of local meteorologyon air quality using generalized additive modelsrdquoAtmosphericEnvironment vol 45 no 6 pp 1328ndash1336 2011

[6] S Mathew Wind Energy Fundamentals Resource Analysisand Economics Springer Berlin Germany 2006

[7] S Mentes Ruzgar Enerjisi ve Sistemleri Ders Notları ITUUccedilak Uzay Fakultesi Istanbul Turkey 2008

[8] P Gipe Wind Power Chelsea Greer Publishing CompanyWhite River Junction VT USA 2003

[9] H Badihi and Y Zhang ldquoPassive fault-tolerant cooperativecontrol in an offshore wind farmrdquo Energy Procedia vol 105pp 2959ndash2964 2017

[10] November 2017 httpsdaskuzawordpresscomarapgir-cografyasi-1

[11] November 2017 httpwwwarapgirgovtrcografi-durumu[12] J Kim and B Yum ldquoSelection between Weibull and log-

normal distributions a comparative simulation studyrdquoComputational Statistics and Data Analysis vol 53 no 2pp 477ndash485 2008

[13] J F Manwell J G Morgan and A L Rogers Wind EnergyExplained 3eory Design and Application John Wiley andSons Ltd West Sussex UK 2002

[14] S Rehman andNM Al-Abbadi ldquoWind shear coefficients andtheir effect on energy productionrdquo Energy Conversion andManagement vol 46 no 15-16 pp 2578ndash2591 2005

[15] H Lettau ldquoNote on aerodynamic roughness-parameter es-timation on the basis of roughness-element distributionrdquoJournal of Applied Meteorology vol 8 no 5 pp 828ndash8321969

[16] E K Akpinar and S Akpinar ldquoAn assessment on seasonalanalysis of wind energy characteristics and wind turbinecharacteristicsrdquo Energy Conversion and Management vol 46no 11-12 pp 1848ndash1867 2005

[17] A Senkal and N S Ccediletin Turkiyersquode kurulu olan buyuk guccedilluruzgar santrallerinin kapasite faktorlerine genel bir bakıs EgeBolgesi Enerji Forumu Denizli Turkey 2009

[18] F Kose andM Ozgoren ldquoRuzgar Enerjisi Potansiyeli Olccedilumuve Ruzgar Turbini seccedilimirdquo Muhendis ve Makine vol 551pp 20ndash30 2005

[19] S Krohn P E Morthorst and S Awerbuch3e Economics ofWind Energy 3e European Wind Energy Association ReportEuropean Wind Energy Association Brussels Belgium 2009

[20] P Nielsen Offshore Wind Energy Projects Feasibility StudyGuidelines SEAWIND-Altener project 41030Z01-2001Ver 30 EMD International AS Aalborg Denmark 2003

[21] C Camporeale Offshore Wind Farms in Italy 3e Sicily CaseOwemes Citavecchia Italy 2006

[22] ODE Study of the Costs of Offshore Wind GenerationmdashAReport to the Renewables Advisory Board (RAB) amp DTI URNNUMBER 07779 Government publication 114 pagesLondon UK 2007

[23] EWEA Financing and Investment Trends 2016 Vol 9 EWEABrussels Belgium May 2017

Advances in Meteorology 9

5 Conclusion

In this study average wind speed and wind direction data ofArapgir province measured at 10m height and 10-minuteintervals and taken from OMGI (Automatic MeteorologicalObservation Station) were analyzed by WAsP software Inthe analysis made the average wind speed is found as275ms the average power density is found as 53Wm2 thefigure parameter is found as 151 and the scale parameter isfound as 42ms obtained at 10m according to the Weibulldistribution

As a result of the calculations made in WAsP 12 sectorshave been used in the wind mill and dominant wind di-rection has been observed as SE at 120deg sector e change ofthe Hellmann coefficient as per the direction by usingexisting data in addition to the analysis made as per the windblowing has been examined and the average Hellmanncoefficient has been calculated as 034

As a result of this micrositing study with 12MW in-stalled power where 6 VESTAS V80 wind turbines are usedwith the wind power plant to be established in the region ithas been calculated that annual gross 41316GWh and net38352GWh electricity shall be generated It has been un-derstood that 717 loss shall occur as a result of the in-terference of wind turbines with each other

In case six 2MW turbines operate under nominal powerduring 8760 hours a maximum of 105120GWh energy maybe generated As a result of the calculations made in WAsPit has been determined that the wind power plant formed bysix 2MW turbines shall generate 38352GWh energy in oneyear and the capacity factor shall be realized as 3648

Annual generation values of the plant have been found as426 eurockWh by using 3648 capacity factor calculated forthe VESTAS V80 turbine used in the established systemeamortization period is calculated as 7 years Unpredictableexpenditure was selected as 2 of the initial installation coste height of the tower of the wind turbine was determinedas 80m e wind speed at 80m height was calculated as71ms e power density at the height of 80m was cal-culated as 309 In Figure 10 the final relevance factor ofrunning the system in the MatlabSimulink model is 8357

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors certify that there are no actual or potentialconflicts of interest in relation to this article

References

[1] October 2017 httpswwwmgmgovtrgenelruzgar-atlasiaspx[2] A Bechkaoui A Ameur S Bouras and A Hadjadj ldquoOpen-

circuit and inter-turn short-circuit detection in PMSG forwind turbine applications using fuzzy logicrdquo Energy Procediavol 74 pp 1323ndash1336 2015

[3] E T Karagol and I KavazDunyada ve Turkiyersquode YenilenebilirEnerji Turkuvaz Haberlesme ve Yayıncılık AS Vol 197Istanbul Turkey 2017

[4] E Kovac-Andriḉ T Radanoviḉ I Topaloviḉ B Markoviḉand N Sakaḉ ldquoTemporal variations in concentrations ofozone nitrogen dioxide and carbon monoxide at OsijekCroatiardquo Advances inMeteorology vol 2013 Article ID 4697867 pages 2013

[5] J L Pearce J Beringer N Nicholls R J Hyndman andN J Tapper ldquoQuantifying the influence of local meteorologyon air quality using generalized additive modelsrdquoAtmosphericEnvironment vol 45 no 6 pp 1328ndash1336 2011

[6] S Mathew Wind Energy Fundamentals Resource Analysisand Economics Springer Berlin Germany 2006

[7] S Mentes Ruzgar Enerjisi ve Sistemleri Ders Notları ITUUccedilak Uzay Fakultesi Istanbul Turkey 2008

[8] P Gipe Wind Power Chelsea Greer Publishing CompanyWhite River Junction VT USA 2003

[9] H Badihi and Y Zhang ldquoPassive fault-tolerant cooperativecontrol in an offshore wind farmrdquo Energy Procedia vol 105pp 2959ndash2964 2017

[10] November 2017 httpsdaskuzawordpresscomarapgir-cografyasi-1

[11] November 2017 httpwwwarapgirgovtrcografi-durumu[12] J Kim and B Yum ldquoSelection between Weibull and log-

normal distributions a comparative simulation studyrdquoComputational Statistics and Data Analysis vol 53 no 2pp 477ndash485 2008

[13] J F Manwell J G Morgan and A L Rogers Wind EnergyExplained 3eory Design and Application John Wiley andSons Ltd West Sussex UK 2002

[14] S Rehman andNM Al-Abbadi ldquoWind shear coefficients andtheir effect on energy productionrdquo Energy Conversion andManagement vol 46 no 15-16 pp 2578ndash2591 2005

[15] H Lettau ldquoNote on aerodynamic roughness-parameter es-timation on the basis of roughness-element distributionrdquoJournal of Applied Meteorology vol 8 no 5 pp 828ndash8321969

[16] E K Akpinar and S Akpinar ldquoAn assessment on seasonalanalysis of wind energy characteristics and wind turbinecharacteristicsrdquo Energy Conversion and Management vol 46no 11-12 pp 1848ndash1867 2005

[17] A Senkal and N S Ccediletin Turkiyersquode kurulu olan buyuk guccedilluruzgar santrallerinin kapasite faktorlerine genel bir bakıs EgeBolgesi Enerji Forumu Denizli Turkey 2009

[18] F Kose andM Ozgoren ldquoRuzgar Enerjisi Potansiyeli Olccedilumuve Ruzgar Turbini seccedilimirdquo Muhendis ve Makine vol 551pp 20ndash30 2005

[19] S Krohn P E Morthorst and S Awerbuch3e Economics ofWind Energy 3e European Wind Energy Association ReportEuropean Wind Energy Association Brussels Belgium 2009

[20] P Nielsen Offshore Wind Energy Projects Feasibility StudyGuidelines SEAWIND-Altener project 41030Z01-2001Ver 30 EMD International AS Aalborg Denmark 2003

[21] C Camporeale Offshore Wind Farms in Italy 3e Sicily CaseOwemes Citavecchia Italy 2006

[22] ODE Study of the Costs of Offshore Wind GenerationmdashAReport to the Renewables Advisory Board (RAB) amp DTI URNNUMBER 07779 Government publication 114 pagesLondon UK 2007

[23] EWEA Financing and Investment Trends 2016 Vol 9 EWEABrussels Belgium May 2017

Advances in Meteorology 9

[24] WindEurope Wind Energy Will Reduce Costs Sharply overNext Five Years IEA Report Finds Vol 27 WindEuropeBrussels Belgium 2016

[25] WindEurope Costs Come down Again in 1 GW GermanOnshore Wind Auction Vol 23 WindEurope BrusselsBelgium 2017

[26] WindEurope Avoiding Fossil Fuel Costs with Wind Energy AReport by the European Wind Energy Association WindEu-rope Brussels Belgium 2014

10 Advances in Meteorology

Hindawiwwwhindawicom Volume 2018

Journal of

ChemistryArchaeaHindawiwwwhindawicom Volume 2018

Marine BiologyJournal of

Hindawiwwwhindawicom Volume 2018

BiodiversityInternational Journal of

Hindawiwwwhindawicom Volume 2018

EcologyInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2018

Forestry ResearchInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

International Journal of

Geophysics

Environmental and Public Health

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

International Journal of

Microbiology

Hindawiwwwhindawicom Volume 2018

Public Health Advances in

AgricultureAdvances in

Hindawiwwwhindawicom Volume 2018

Agronomy

Hindawiwwwhindawicom Volume 2018

International Journal of

Hindawiwwwhindawicom Volume 2018

MeteorologyAdvances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018Hindawiwwwhindawicom Volume 2018

ChemistryAdvances in

ScienticaHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Geological ResearchJournal of

Analytical ChemistryInternational Journal of

Hindawiwwwhindawicom Volume 2018

Submit your manuscripts atwwwhindawicom

Hindawiwwwhindawicom Volume 2018

Journal of

ChemistryArchaeaHindawiwwwhindawicom Volume 2018

Marine BiologyJournal of

Hindawiwwwhindawicom Volume 2018

BiodiversityInternational Journal of

Hindawiwwwhindawicom Volume 2018

EcologyInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2018

Forestry ResearchInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

International Journal of

Geophysics

Environmental and Public Health

Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

International Journal of

Microbiology

Hindawiwwwhindawicom Volume 2018

Public Health Advances in

AgricultureAdvances in

Hindawiwwwhindawicom Volume 2018

Agronomy

Hindawiwwwhindawicom Volume 2018

International Journal of

Hindawiwwwhindawicom Volume 2018

MeteorologyAdvances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018Hindawiwwwhindawicom Volume 2018

ChemistryAdvances in

ScienticaHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Geological ResearchJournal of

Analytical ChemistryInternational Journal of

Hindawiwwwhindawicom Volume 2018

Submit your manuscripts atwwwhindawicom