distribution generation with fuzzy

International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 2012

DOI : 10.5121/ijfls.2012.2304 41

PERFORMANCE OF FUZZY LOGIC BASEDMICROTURBINE GENERATION SYSTEM

CONNECTED TO GRID/ISLANDED MODESanjeev K Nayak1 and D N Gaonkar2

1&2Department of Electrical and Electronics Engineering, National Institute ofTechnology Karnataka, Surathkal Mangalore, INDIA

[email protected]@gmail.com

ABSTRACT

The microturbine generation (MTG) system are becoming the popular source of distributed generation(DG) due to their fuel flexibility, reliability and power quality. The MTG system is a complicatedthermodynamic electromechanical system with high speed of rotation, frequency conversion and its controlstrategy. In spite of several techniques to control high speed of microturbine is not accurate and reliabledue to their anti-interference problem. This paper presents a fuzzy logic controlled MTG system for gridconnected and islanded mode of operation. The development of fuzzy logic based speed governor includesinput and output membership function with their respective members. The load variation on MTG system isperformed using conventional PI controller and fuzzy logic controller are implemented in Matlab/Simulinkand their results are compared with each other. The simulation result of MTG system shows theperformance improvement of fuzzy logic governor over a conventional governor.

KEYWORDS

Distributed Generation, Microturbine PMSM, Power Converter, and Fuzzy logic.

1. INTRODUCTION

Distributed Generation (DG) is predicted to play an increasing attention in the electric powersystem of the near future. DG is limited size of generation (25kW to 1MW) and interconnected atthe substation, distribution feeders or consumer load levels [1]. The microturbine generation(MTG) system is a typical and practical system of DG source, because of its small scalegeneration with fuel flexibility, reliability and power quality. It has been applied in various fields,such as peak power saving, co-generation, remote and premium power applications [2]. The MTGsystem composed of microturbine as a prime mover, permanent magnet synchronous machine(PMSM), power interfacing circuit for frequency conversion between generation and load. Amicroturbine is thermodynamically complex mechanical system cannot be modelled accuratelyand it is difficult to control the speed [2]. A microturbine is integrated with PMSM produces highfrequency AC power, where the operating speed ranges generally between 50,000 to 120,000rpm. The power produced at the generator is around 1 k to 2 kHz of frequency, this highfrequency AC power is convertedto DC then inverted back to 50 or 60Hz AC power usingsuitable converter and inverter [3]. The nonlinear controlled methods based on model cannotsolve the various control problems on MTG system, some advance control method as have beenapplied such as incremental fuzzy PI controller, fuzzy PID controller and neural networkcontrollers. Controlled algorithm based on fuzzy logic have been implemented in many processesthe application of such control techniques has been motivated by the following reasons, 1) itimproves the robustness over a conventional linear control algorithm, 2) Simplified controldesign for difficult system model 3) Simplified implementation[4]. This paper presents a fuzzy

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42

logic controlled speed governor and conventional transfer function governor for a microturbine topredict the performance of fuzzy logic controller. The microturbine is used as MTG system onceit is compared with nominal controller connected to the grid and isolated mode of DG system.The simulation result shows the performance of fuzzy logic controller for different load on MTGsystem.

2. MICROTURBINE GENERATION (MTG) SYSTEM

Microturbines are smaller version of heavy duty gas turbines compact in size and componentslike compressor, heat exchanger, burner and turbine. Basically there are two types ofmicroturbines, classified based on construction and location of its components. One is a highspeed single shaft design with compressor and the turbines are mounted on the same shaft usuallythe PMSM is use to generate the electrical power. Another is split shaft design that uses a powerturbine rotating at 3600 rpm and a conventional generator (usually induction generator orsynchronous generator) connected via a gearbox [6]. The microturbine system presented in thispaper is based on gas turbine model presented by W I Rowen[5] which was successfully adopteras microturbine generation(MTG) system by Huang Wei[6] connected in parallel to themicrogrid, and used as DG system by Gaonkar[7] and Guda S R[8] in isolated mode. Theturbine model was proposed by W I Rowen[5] is to be adopted as single shaft microturbine withfuzzy logic speed governor implemented in MATLAB/Simulink is shown in Figure.1, and thesame is used as prime mover for the MTG with PMSM and power electronics circuit interfacingsystem shown in Figure.2.

LOW

+

+G1

1+sT1G2

1+sT2e-sT

e-sT

f2(u)

f1(u)

+

+

-

G51+sT5+

+

Tr

0.8

G41+sT4

+

-

3.3s+10.5s

dudt

RefSpeed(pu)

+

- K2

K1

0.23

K3X

G31+sT3f3(u)

f4(u)

+

+

100s

+ -0.01 dudt

1JT

Rotor Inertia

Speed Governor

Tempreture controller

Fuel & Combustion

Exhaust system

Turbine dynamics

Rotor Speed

Acceleration controller

Reference Temp

Figure.1. Fuzzy logic controlled microturbine

Rectifier Inverter

Burner

Heat exchanger

Compressor Turbine

PMSM

C

Fuel

Ambient airExhaust air

Valve

L

Controller

PLL

Rt Lt

GRID

Load

Speed

Ref, Speed

Fuzzy Logic

Figure.2 Fuzzy logic controlled MTG system


43

The linear pattern is utilizes as generator model applicable for transient analysis and the modelcomprises temperature controller, fuel control, turbine dynamics, speed governor and accelerationcontroller blocks [7]. The speed governor for a microturbine can be divided into droop regulationand non-drop regulation, which is utilized and designed for the purpose of adopting therequirement of different load characteristics. Speed controller is usually modelled using lead-lagtransfer function or by PID controller. In this work a fuzzy logic based governor has been used asspeed and change in speed are input member ship function, regulated speed as output membershipfunction. The membership function can adjusted so that the governor can act with droop or asisochronous governor [7][8]. The fuel flow is controlled as a function of Vce are shown in aseries of blocks including the valve position and flow dynamics, the output of low value selector(represented by Min in MATLAB/Simulink) is the lowest of the three inputs and results in theamount of fuel to the compressor-turbine, and Vce represents the final amount of fuel demand forthat particular operating point and is an input to the fuel system [6][8]. The slow dynamicsperformance of the MTG system is focused here based on the simplified model is built in with anassumption, i.e. MTG system is operating under normal condition by neglecting the fast dynamicsof microturbine (start up, shut down, internal faults, sudden loss of power etc). In addition to thatthe proposed model employees per unit system to represents the microturbine as its controlsystem with expectation of temperature, each important control block is discussed as subsectionin[7] [8]. The model presented in this paper is concentrates on slow dynamics of MTG system.

3. FUZZY LOGIC

Fuzzy logic controller is rule based controller where a set of rules represents a control decisionmechanism to correct the effect of certain cause used for generation systems. In fuzzy logic, thelinguistic variables are expressed by fuzzy sets defined on their respective universe discourse, toovercome the difficulties of soft controlling fuzzy logic found to be effective alternating toconventional control techniques[9]-[10]. The configuration of fuzzy logic based system into fourparts they are, Fuzzification, Knowledge Base, Interface Mechanism and Defuzzification. Thelead lag transfer function compensator is replaced by the equivalent fuzzy logic based speedgovernor, the design of PI-like fuzzy knowledge base controller works on the area control error(ACE) and change in area control error (∆ACE) is considered as input to the fuzzy logiccontroller. For the automatic generation control problem the input to fuzzy controller for ith areaat a particular instant are ACEi(t) andlinguistic variables as VHS: Very High Speed, HS: High Speed, NC: No Change, NS: NormalSpeed, LS: Low Speed, VLS: Very Low Speed, LN: Large Negative, N: Negative, P: Positive,LP: Large Positive, VL: Very Low, L: Low, Z: Zero, H: High, VH: Very High- respectively. Allthe variables of ACE, ∆ACE and ∆U are considered in a symmetrical triangular membershipfunction. The membership function of ACE over the operating range of minimum and maximumvalues of ACE is shown in Figure.3 (a) (b) and (c). The membership function would perform amapping from the crisp values to a fuzzified value, One such particular crisp input ACE isconverted to fuzzified value i.e. 0.8/VHS + 0.2/HS where 0.8 and 0.2 are membership grademembership function.

0.5

0

VHS HS NS LS VLS

0.2

0.81 1

0.5

0

VL L Z H VH 1

0.5

0

LN N NC P LP

Figure.3 a) Speed b) Change in speed c) Reg, speed


44

Corresponding to the linguistics variables VHS and HS in fuzzy system a membership grades arezero for all other linguistic values except VHS and HS. Here the crisp value input to the systemwill be converted to fuzzified value considering several membership grades corresponding toeach linguistic variable. Same way the other input of ∆ACE and ∆U are fuzzified. The output ofthe inference mechanism is a fuzzy value, and hence it is necessary to convert the fuzzy value to(crisp) real value sine, the physical process can’t deal with fuzzy values. This operation which isinversely fuzzified is known as defuzzification. The well known centre of defuzzification methodhas been used for its simplicity. The major drawback of Mean of Maximum (MOM) method isthat it does not use all the information converted by the fuzzy command and hence it becomesdifficult in generating commands that run the system smoothly.

)(

)*(

PI

PI

PI

ofMembership

ingcorrespondOutputMembershipU ∑Σ=∆

j

UjjU

ΣΣ=∆ *

(1)

∆U is determined using the centre of gravity method µj is the membership value of thelingivustic variables recommending the fuzzy control action, Uj is the precise numericalvalue corresponding to the fuzzy control action. The ∆U obtained from (9) is added withthe existing previous signal to obtain the actual output signal U which is given to themicroturbine [11].

Table. 1 Fuzzy logic tables

S p e e d

S p e e d V L L Z H V H

V L S L N L N L N N N C

L S

N S

H S

V H S

L N

L N

N

N C

L N N CN P

N N C P L P

N C P L P L P

P L P L P L P

3. MODELLING OF PMSM

Based on the d-q reference frame, the stator voltage equitation of PMSM can be described as[12].

+−=

+−=

qsdq

q

dsqd

d

irdt

du

irdt

du

(2)

Where, ud, & dq are d and q components of the stator winding voltage, ψd & ψq are dand q components of stator flux, id ,iq are d q component of stator winding current, ω is


45

the angular speed, rs is the stator resistance. The flux linkage of PMSM can be expressedas,

=+=

qqq

PMddd

iL

iL

(3)Where, Ld, Lq are d and q axis inductances, ψPM is the permanent magnet rotor fluxlinkage, The electromagnetic torque equation of the PMSM is given as [12],

( )dqqde iipT −=23

(4)

Where p is the number of pole pairs, the motion equation is expressed as,

Le TBdt

dJT +Ω+Ω=

(5)

p=Ω

(6)

Where, ψPM is the rotor flux linkage, J is the moment of inertia, B is the damping torqueand TL is the motor load.

3. POWER ELECTRONICS CONVERTER

The power electronics interfacing is a critical component in the single shaft microturbine designand represents significant design challenge, especially in matching turbine output power to theconnected load or grid. There are different configurations available to interface the MTG powerto load. One possible is to use a three phase diode rectifier, voltage source invert and filter. Thisrequires a separate start-up arrangement for the microturbine. The configuration used in this paperis assumed to be brought to rated speed for the isolated mode of operation and bidirectionalpower converter has been used for the grid connected mode of operation.

For a single shaft microturbine the power interfacing circuit is used to convert high frequency ACpower produced by the PMSM into usable electrical power. The power conditioning circuit is oneof the critical components in a single shaft microturbine design and represents significantchallenge in design. Especially in matching turbine output to required load. The control structurefor grid connected mode of operation is shown in Figure 4. The grid side converter operates as acontrolled power source. The standard PI controller is used to regulate the grid current in the dqsynchronous frame in the inner control loops and the DC voltage regulator in the outer loop. It isseen that a PI controller regulates the DC bus voltage by imposing an id current component. Idrepresents the active power component of the injected current into the grid and iq is it reactivecomponent. In order to obtain only a transfer of active power only a transfer of active power, theid current reference set to zero. The decoupling terms are used to obtain independent control of idand iq in. A PLL is used to synchronize the converter with the grid. The philosophy of the PLL isthat the difference between the grid phase and inverter phase angel can be reduced to zero usingPI controller and locking the line side inverter phase to grid [8].


46

Figure 4: Line side converter control for grid connected mode

For the isolated mode of operation the power conditioning unit consist of a three phase dioderectifier, a voltage source inverter (VSI) and LC filter are used. The control system for a voltagesource inverter is shown in Figure 5. A 166 Hz, voltage source feeds a 50Hz, 50kW load throughan AC/DC/AC converter. The 480V, 166Hz AC power is first rectified by six pulse diode bridgerectifier and filtered. DC link voltage is given to an IGBT two level inverter generating 50Hz.The IGBT inverter uses Pulse Width Modulation (PWM) with 2 kHz carrier frequency. Thevoltage is regulated at 1p.u.(480 Vrms) by a PI voltage regulator using abc to dq and dq to abctransformation. The first output voltage regulator is a vector containing the three modulationsignals used by the PWM generator to generate six IGBT pulses [8].

Figure 5: Line side converter control for isolated mode

3. SIMULATION RESULTS

Before connection the MTG system to an electrical load, the performance of microturbinecontroller is verified by the fuzzy logic and nominal PI controller in Matlab/simulink. Form theperformance test of microturbine it is ascertained that, the fuzzy logic based controller settlingtime is less than nominal PI controller and also the oscillation are eliminated in fuzzy logiccontroller. The comparison study of fuzzy logic and nominal PI controller are considered for thefuel demand, turbine torque, speed and power. The simulation is performed for a total duration oftime t=80 second. The turbine is loaded in steps of half load and full load respectively. Thevariation of fuel demand and turbine torque with fuzzy logic and nominal PI controller are shownin Figures 6 and 7 respectively. From the reported Figures 6 and 7 it is observed that, the settling


47

time of fuzzy logic controller is less then PI controller. With respect to fuel flow, the variation ofturbine speed and power are shown in Figures 6 and 7.

0 10 20 30 40 50 60 70 800

0.2

0.4

0.6

0.8

1

1.2

1.4

Time (s)Fu

el d

eman

d (p

.u)

Fuzzy logic controlerPI contrller

19 20 21 22 23 24 25 260.2

0.4

0.6

0.8

60 61 62 63 64 65 66

0.4

0.6

0.8

1

Figure.6 Fuel demand

0 10 20 30 40 50 60 70 80-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Time (s)

Turb

ine

torq

ue (p

.u)

21 22 23 24

0

0.2

0.4

0.6

62 64 66

0.4

0.5

0.6

0.7

0.8

0.9

Fuzzy logic controllerPI controller

Figure.7 Turbine torque

0 10 20 30 40 50 60 70 800.95

0.96

0.97

0.98

0.99

1

1.01

Time (s)

Turb

ine

spee

d(p.

u.)

Fuzzy controllerPI controller

40 41 42 43 44 45

0.96

0.97

0.98

62 63 64 65

0.9650.97

0.9750.98

0.985

Figure 8. Turbine speed

0 10 20 30 40 50 60 70 80-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Time (s)

Turb

ine

pow

er (p

.u)

Fuzzy logic controllerPI controller

18 20 22 24 26

0

0.2

0.4

0.6

61 62 63 64 65 66 67

0.4

0.5

0.6

0.7

0.8

0.9

Figure 9. Turbine power

3.1 GRID CONNECTED MODE

The MTG system in connected to the grid is supplying a power to the grid is predicted on thebasis of active and reactive power. The active and reactive power supplied by the fuzzy logiccontrolled MTG system is shown in Figure.10. From the reported Figure.10 it is observed that,the active power is 50kW and reactive power is almost zero. Hence it is ascertained that thePMSM supplies only active power. The DC link voltage and turbine torque are shown in Figures11 and 12 respectively. From the reported Figure12 it is observed that the turbine torque is


48

negative in per unit, it means that, the PMSM is running as generator. To generate the turbinetoque the proportional fuel demand and speed are shown in Figures 14 and 15. The inverteroutput voltage and load voltage are shown in Figures 16 and 17 respectively. From the Figures 16and 17 it is observed that, the MTG system is supplying quality power to the grid.

0 1 2 3 4 5 6 7 8-1.5

-1

-0.5

0

0.5

1x 104

Time (s)

Activ

e &

reac

tive

pow

er

Active power in wattsReactve power in Var

Figure.10 Actvie and reactive power

0 1 2 3 4 5 6 7 80

100

200

300

400

500

600

700

Time (s)

DC lin

k vo

ltage

(V)

Figure.11 DC link voltage

0 1 2 3 4 5 6 7 8-1

-0.5

0

0.5

Time (s)

Troq

ue (p

.u)


0 1 2 3 4 5 6 7 8-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Time (s)

Fuel

flow

(p.u

)

Figure.13 Fuel flow

0 1 2 3 4 5 6 7 80.975

0.98

0.985

0.99

0.995

1

1.005

1.01

1.015

Time(s)

Turb

ine

spee

d (p

.u)

Figure.14 Turbine speed

0 1 2 3 4 5 6 7 8-0.4

-0.2

0

0.2

0.4

0.6

Time (s)

Turb

ine

torq

ue(p

.u)


1 2 3 4 5 6 7 8

-500

-400

-300

-200

-100

0

100

200

300

400

500

Time (s)

Inve

rter v

olta

ge (V

)

2.55 2.6 2.65 2.7 2.75 2.8-600

-400

-200

0

200

400

600

Figure.16 Inverter voltage

0 1 2 3 4 5 6 7 8-1.5

-1

-0.5

0

0.5

1

1.5

Time (s)

Load

vol

tage

(p.u

.)

2.045 2.05 2.055 2.06 2.065 2.07 2.075 2.08 2.085

-1

0

1

Figure.17 Load voltage

3.2 Isolated mode

The fuzzy logic controlled MTG system is connected to an isolated mode and it performance issimulated for the total duration of 30sec. The variation of turbine fuel flow and torque are shownin Figure.18 and 19 respectively. From the reported Figures 18 and 19 it is observed that, the fueldemand and turbine torque increase with increase in load and decreases with decrease in load.


49

The variation of turbine speed due to the increase and decrease of load on the MTG system isshown in Figure. 20. The active and reactive power is one of the important parameters of MTGsystem in connected to the load. Since the MTG system power generation by PMSM, it cansupply only the active power. it is observed from the reported Figure.22. It has been measuredbetween phase to ground voltage and current. The variation of load voltage and current betweenphases to ground is shown in Figures 23 and 24 respectively. From the reported Figures 23 and 24it is observed that, the variation of load voltage and current maintains the power quality.

0 2 4 6 8 10 12 14 16 18 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Time (s)

Fuel

dem

and

(p.u

)

Figure.18 Fuel demand

0 2 4 6 8 10 12 14 16 18 20-0.4

-0.2

0

0.2

0.4

0.6

Time (s)

Turb

ine

torq

ue (p

.u)


0 2 4 6 8 10 12 14 16 18 200.975

0.98

0.985

0.99

0.995

1

1.005

Time (s)

Turb

ine

spee

d (p

.u)

Figure.20 Turbine speed

0 2 4 6 8 10 12 14 16 18 20-2000

0

2000

4000

6000

8000

10000

12000

14000

Time (s)

Activ

e &

reac

tive

powe

r

Active power in kWReactive power in Var

Figure.22 Active and reactive power

0 2 4 6 8 10 12 14 16 18 20-300

-200

-100

0

100

200

300

Time (s)

Load

vol

tage

(V)

4.97 4.98 4.99 5 5.01 5.02 5.03

-200

-100

0

100

200

14.96 14.97 14.98 14.99 15 15.01 15.02 15.03 15.04-300

-200

-100

0

100

200

300

Figure.23 Load voltage

0 2 4 6 8 10 12 14 16 18 20-40

-30

-20

-10

0

10

20

30

40

Time (s)

Load

cur

rent

(A)

9.98 9.99 10 10.01 10.02 10.03-50

0

50

14.98 14.99 15 15.01 15.02

-40

-20

0

20

40

Figure.24 Load current

4. CONCLUSIONS

The Fuzzy logic controlled MTG system connected to the grid and isolated load has beenimplemented and simulated using Matlab/Simulink. Initially the microturbine controller verifiedby the both nominal PI controller and fuzzy logic controller. Form the simulation study, the fuzzylogic controller is preferred over a nominal PI controller due to minimum oscillation and steelingtime. The fuzzy logic based verified microturbine is used for the MTG system in connected tothe grid and isolated model of operation. The developed model of MTG system includesmicroturbine, PMSM and power electronics interfacing circuit. From the simulation study of theMTG system is supplying power to the grid and the performance of load fallowing character isascertained. The fuzzy logic controlled MTG system will maintain the quality power and fewerharmonic. From the simulation study it is observed that, the active and reactive power injected bythe MTG system for grid connected as well as isolated mode of operation.


50

REFERENCES

[1] P. P. Barker, and R. W. de Mello, 2000, Determining the impact of distributed generation on powersystems: Part 1- Radial Distribution systems,” in Proc. IEEE Power Eng. Society. Summer Meeting,vol. 3, pp.1645-1656.

[2] Shijie Yan and Xu Wang “Fuzzy Controlled with Tracking Differentiator for Microturbine” Sixthinternational conference on fuzzy systems and Knowledge Discovery,FSKD-2009 Tianjin,14th -16thAugust 2009, pp. 97–101.

[3] A. Al-Hinai and A. Feliachi, “Dynamic model of microturbine used as a distributed generation” inProc 34th South Eastern Symposiums on system theory, Huntsville, Alabama, 2002,pp.209-213.

[4] Jenica Ileana Corcau and Eleonor Stoenescu “Fuzzy logic controlled as power system stabilizer”International Journal of Circuits, System and Signal Processing, Vol.1, Issue.3, 2007, pp.266-273.

[5] W. I. Rowen, “Simplified Mathematical Representations of Heavy Duty Gas Turbines,” Journal ofEngineering for Power, Trans. ASME, vol. 105, no. 4, pp. 865-869, Oct, 1983.

[6] Hung Wei, Wu Ziping, Niu Ming, Zhang Jianhua, Guo Yuanbo and Wu Chong “Dynamic Modellingand Simulation of Microturbine Generation System for the parallel operation of microgrid”International Conference on Sustainable Power Generation and Supply,2009 SUPERGEN-09,Nanjing, 6th -7th April 2009, pp.1-8.

[7] D. N. Gaonkar and R N Patel “Modeling and Simulation of Microturbine Based DistributedGeneration System” IEEE Power India Conference 2006, New Delhi, 5th June 2006. pp.05.

[8] S. R. Guda. C .Wang and M H. Neherir, “Modeling of Microturbine Power Generation Systems”Electric Power Components and systems,1532-5016, Vol.34, Issue.9,pp.1027-1041,2006.

[9] U. Yolac and T Yalcinoz “Comparison of fuzzy logic and PID controller for TCSC using MatLab”39th IEEE International Conference on Power Engineering, Vol.1,2004,pp.438-442.

[10] B Anand and Ebenezer Jeyakumar “Fuzzy logic based load frequency control of hydro thermalsystem with non-linearity” International Journals of Electrical and Power Engineering,Vol.3,Issue.2,2009,pp.112-118.

[11] Shijie Yan and Xu Wang “Active Disturbance Rejection Fuzzy Control of MTG Fuel pressure” IEEEInternational Conference on Mechatronics and automation, 9th -12th August 2009 Changchum,China.

[12] J. B Ahn, Y. H. jeong, D.H. Kang and J.H.Park “Development of High Speed PMSM for DistributedGeneration Using Microturbine” 30th IEEE Annual Conference on Industrial Electronics Society,November 2-6,2004,Busan, Korea.

Authors

Sanjeev K Nayak received his (M Tech degree from UBDT College of Engineering Davengere,Kuvempu University Shimoga Karnataka, INDIA in the year 2007, He worked as a faculty in theDepartment of Electrical and Electronics Engineering, at Nitte Meensakashi Institute of Technology,Bangalore for a duration of three years. Currently he is pursuing his Ph.D research in the Department ofElectrical and Electronics Engineering at National Institute of Technology Karnataka Surathkal. His areasof research interests are Distributed Generation-Microturbine, Fuel cell and Power Quality. He publishedpaper in national and international conference, in INDIA, Saudi Arabia and Thailand.

D. N. Gaonkar (M’2008) has received his Ph.D. degree from the Indian Institute of Technology Roorkee,India; in the year 2008. He was a visiting research scholar at the University of Saskatchewan Canada in theyear 2008. He has edited and written a chapter in the book titled DISTRIBUTED GENERATION, which ispublished by INTECH publication Austria. He has published many papers in international journals andconferences. Presently he is working as an Assistant Professor in the Department of Electrical Engineering,National Institute of Technology Karnataka, Surathkal, Mangalore, INDIA. His research areas of interestare Power System Operation and Control, Power Electronics and Distributed Generation Systems.

distribution generation with fuzzy

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