transmission line protection scheme in presence of · 2018. 11. 26. · key words : islanded...

TRANSMISSION LINE PROTECTIONSCHEME IN PRESENCE OF

MICROGRID USINGNEURO-WAVELET ANALYSIS

S.CHANDRA SHEKAR 1, Dr.G.RAVI KUMAR 2,Dr.S.V.N.L. LALITHA3

1Research Scholar & Associate Professor, 2,3ProfessorDepartment of EEE,

1,3 Koneru Lakshmaiah Education Foundation,Andhra Pradesh, India,

1Anurag Engineering College, Kodad, Telangana, India,2Bapatla Engineering college, Bapatla,

[email protected],[email protected]

[email protected]

October 12, 2018

Abstract

A novel protection scheme is introduced in this paper forthe protection of transmission network with microgrid (MG)having distributed generators (DG’s) such as wind, solarPV, battery and fuel cell sources. MG’s give environmen-tal, clear economical benefits to end consumers, society andutilities. Transmission network and MG’s includes techni-cal challenges where the challenge of MG protection schemeis to act rapidly to utilitygrid as well as MG faults. Thispaper implemented a MG model and it is interconnected toa transmission system then Wavelet Multi Resolution Anal-ysis (WMRA) is used to detect, discriminate and locality of

1

International Journal of Pure and Applied MathematicsVolume 120 No. 6 2018, 551-565ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

551

faults. Faults in the transmission system are detected by afault index and compared to threshold value and for identi-fying the locality of fault Artificial Neural Networks (ANN)is used in transmission network with MG. This implementedalgorithm is significant after being tested to detect, discrim-inate and locality of faults at transmission network intercon-necter of MG which is nearly irrespective of fault inceptionangle (FIA), fault impedance and distance of feeder.

Key Words: Islanded microgrid, Wavelet Multi Reso-lution Analysis, Microgrid protection scheme, inverter-interfacedmicrogrids.

1 INTRODUCTION

DG’s are very close to the loads have a benefit of optimising net-work complexity, transmission losses and will reduce its cost due tolower network capacity, operation costs and operation losses whichincreases reliability of operation [1]. However, MGs have differentchallenges where the major hurdle is providing protection to a gridsystem [2]. Mainly there are 2 challenges: Firstly, the conditionsof islanding during fault of MG from utilitygrid. Secondly, propersynchronization and reliable method of protection for the effectivetripping under fault condition [3]. A detailed D1-coefficients ofoutput signals of current using mother wavelets Bior1.5 is used todetect, discriminate and locality of fault by applying ANN [4]. Inthis paper, neuro-wavelet based fault location schemes are appliedon a system with MG. After the MG model is designed, it is linkedto a transmission system and a fault is simulated on the transmis-sion network so that fault description and location algorithm canbe applied for detecting and location estimation of fault in MG en-vironment. This method is applied on typical transmission modelfor various faults by varying fault impedances and FIA.

2 TECHNICAL CHALLENGES IN MI-

CROGRID PROTECTION

Protection scheme of MG must respond for utilitygrid and MGfaults. Firstly, the protection scheme has to disconnect MG from

2

International Journal of Pure and Applied Mathematics Special Issue

552

utilitygrid quickly to protect the MG loads. Secondly, it must iso-late the low capacity part of MG as early as possible for clearingthe fault in the system. Few concerns relating to a protection ofDG’s and MG’s with a huge incursion of DG’s. It is observed thatcalculation of short-circuit current for radial feeders with DG’s userelays of over-current protection depend on a point of common cou-pling (PCC) and a feed-in power from DG’s [5]. Because of this,the amplitude and direction of short circuit currents vary accord-ing to abnormalities in the system. In fact, because of intermit-tent sources (wind and solar PV) periodic variation of loads theoutput of MG is continuously altering. Network topology also beregularly keep changed aimed to approach an operational or eco-nomic targets or loss reduction. To cope up with power flows inbi-directional and lower short-circuit currents in MG’s dominatedby DG’s with power electronic devices interface a novel protectionschemes are necessary, where the relays setting to be checked andupdated methodically that they are still effective [6]. A new adap-tive MG protection scheme concept is developed in this paper usingadvanced communication system [7], the data is taken from off-lineshort circuit analysis and real-time measurements. This new con-cept is developed according to relay settings with and MG topology,generation of power and load condition. The concept of MG has toovercome major challenges in various fields only not for the protec-tion perspective, but also in view of control and delivery [7].

3 WAVELET TRANSFORM

Wavelet Transform (WT) is a linear transformation like FourierTransform, the only significant difference is for a given signal itpermits time localization of different frequency components. So,it is a mathematical method application in signal analysis wherethe signals have transients or irregular discontinuities the waveletconcepts are used for analyzing signal particularly more efficientlike the post faults voltage /current waveform. WT is the tool itdivides the data into different components of frequency, and thentraces each signal with a resolution matched to its particular scale.[8]. In this entire process there are four filters: LPFs L and L’,HPF’s H and H’.

3


553

Fig.1 Wavelet transforms signal analysis.

These wavelet components are useful to detect and categorizethe source of signals of transient surges under abnormal situations[9]. Hence, the WT is used for analyzing transients or irregulardiscontinuities signals in power system.For a function f(t), its continuous wavelet transform is obtained as

W.T (a, b) =1√a

∫x(t)g

(t− ba

)dt (1)

Where a is dilation, b is translation constants and x(t) is the waveletfunction.

4 ARTIFICIAL NEUTRAL NETWORK

ANN is a simplification of real networks of neurons. ANN containsmany inputs in parallel and process the information in a distributedmanner. The stored information in ANN is processed by many ofits processing units. ANN obtain knowledge from learning pro-cess. There is a very inadequate attention to the use of ANN forthe protection of transmission network. Fault distance locality intransmission network and also detects the fault A. S. Oshaba andE. S. Ali use radial basis function ANN but the network doesn’trecognize the phase where the fault occurs.

4


554

Fig.2 Radial basis function of ANN.

5 HYBRIDMICROGRID CONNECTED

TO UTILITYGRID MODELLING

DG’s have involved power sectors for power generation that unifiedat the PCC to the utilitygrid with an aimed to approach an oper-ational or economic targets or loss reduction to improve reliabilityoperation of power delivery against the load demand. Solar PV andwind sources both are random and dependent on changes in condi-tion of climatic. This problem is takeover by integrating all DG’sto form hybrid MG system where the strength of one source over-comes the drawback of the other source. However, the integratingof MG with these DG’s makes some power quality and islandingissues which must be detected, analyzed efficiently. Photovoltaic(PV) system has an array of cells consists of solar PV material inwhich solar radiation converts into direct current and further dc isconverted into the alternating current through inverter then it islinked to utilitygrid. Maximum power obtained from solar PV isproportional to solar irradiance intensity [10]. The output currentis difference between light generated current IL and diode currentID

I = IL − ID (2)

Wind turbine operates like a prime mover coupled to DC genera-tor. A PMW technique converts output of DC generator 3-Φ AC

5


555

voltage.. Whenever rotor blades strikes the wind, wind turbine ex-tracts maximum K.E from the wind. The coefficient of power Cp

measures amount of energy extracted by the wind turbine [11]. Cp

is the function of Tip Speed Ratio (TSR).

λ = ωmR/V (3)

P = 1/2CPρV3AW (4)

ωm = wind turbine mechanical angular rotor speed, P = Power(W).CP = Power coefficient,Vw = velocity of Wind (m/s), A= rotor disc Swept area (m2), =Air Density (kg/m).

6 MICROGRID FAULT ANALYSISWITH

SYSTEM MODELING

Fault analysis in a MG can be considered into two type’s one faultin utilitygrid and other fault in MG termed as internal and externalfaults. The MG need to be operated in utilitygrid connected andislanded mode of operation [12]. Here, the conventional protectionscheme doesn’t provide effective solution, but it needs advancedprotection scheme. In grid connected mode due to role of host gridthe fault currents are large. This allows employing over current re-lay [13], but the fact is that due to existence of DG’s the protectionsynchronization may be compromised.

Fig.3 Single line diagram of MG connected to utilitygrid

6


556

Fig 4. Simulation model for proposed system.

A 60km length transmission line is linked between Bus1, Bus2as test case in this paper. At 10km distance of transmission line atBus3 formulated with wind source capacity of 9MVA, 575V througha transformer of 575V/25KV is connected. A Bus4 connected withsolar PV, Fuel cell and battery source capacity of 400KVA con-nected through transformer of 575V/25KV.

7 ESTIMATION OF FAULT LOCATION

Fault locality estimation is done using ANN following to detec-tion and categorization of fault. For this reason a mother waveletBior1.5 is applied to decompose the 3-Φ currents of the local ter-minal over a 1/2 cycle window [14]. A decomposition D1 is used todetect and categorization of fault to get faster assessment of faultlocality. To estimate the fault locality for a line-ground (LG), line-line (LL), line-line-ground (LLG) and line-line-line-ground (LLLG)fault, D1 decomposition of any one faulty phase can be used.The ANN detailed architecture is as follows:Input layer: signals of current detailed D1-coefficients using motherwavelet Bior 1.5 at both ends are used for locality of fault. Numberof Layers: 2, Number of neurons: 10, Transfer function: pure line

7


557

8 RESULT ANALYSIS

Mother wavelet Bior.1.5 is used for analyze 3- currents of the lo-cal terminal to obtain the detailed D1-coefficients over a 1/2 cyclelength of moving window. The detail D1-coefficients are calculatedfrom the Bus1, Bus 2, Bus3 and Bus4 to obtain efficient detailedD1-coefficients. The Fault Index (IF) of each phase is then calcu-lated. The results are plotted for different faults on transmissionnetwork at different FIA are given below.

Fig.5. Variation of FI from Bus1 at line-line-line Fault on phaseAB at FIA 0◦.

8


558

Fig.6.Variation of FI from Bus1 at line-line-line Fault on PhaseAB at FIA 20◦.

Fig.7.Variation of FI from Bus1 at line-line-line Fault on PhaseAB at FIA 40◦.

Fig.8.Variation of FI from Bus2 at line-ground Fault on Phase AGat FIA 80◦.

9


559

Fig.9.Variation of FI from Bus2 at line-line-ground Fault on PhaseABCG at FIA 80◦.

Fig.10.Variation of FI from Bus2 at line-line-line-ground Fault onPhase ABCG at FIA 80◦.

10


560

Fig.11. Detection of line-ground fault at PV generation.

Fig.12. Detection of line-line-ground at PV generation.

Fig.13. Detection of line-line-line-ground fault at PV generation.

11


561

Figures 11-13 Shows the detection of fault at PV generation withLG, LLG and LLLG faults on Phase ABCG.

Table-I: ANN based location of fault Analysis

9 CONCLUSION

This paper describes an novel technique based on WMRA is usedto detection, discrimination and locality of the fault in the trans-mission network with MG comprise of wind, solar PV, fuel cell andbattery energy sources using ANN analysis. The test system is cre-ated and simulated using the power system block with SIMULINKsoftware. The proposed protection scheme tested and found rapid,reliable and accurate for different faults on transmission networkwith various fault impedances, FIA and locality of the fault.

12


562

References

[1] S. M. Brahma, Fault location scheme for a multi-terminaltransmission line using synchronized voltage measurements,IEEE Trans. Power Del., vol. 20, no. 2, pp. 13251331, Apr.2005.

[2] W. E. Feero, et al., ”White paper on protection issues of themicrogrid concept”, 2002.

[3] Oudalova and A. Fidigattibfd, Adaptive Network Protection inMICROGRID, 2011. www.microgrids.eu/documents/519.pdF

[4] Kulkarnisakekar Sumant Sudhir, Wavelet Based Fault Detec-tion & ANN Based Fault Classification In Transmission LineProtection Scheme, International Journal of Engineering Re-search & Technology, Vol. 2 Issue 5, May 2013, pp. 1492-1500.

[5] H. Nikkhajoei and R. H. Lasseter, Microgrid Protection, IEEEPES General Meeting, Tampa, 24-28 June 2007, pp. 1-6.

[6] A. Oudalov, et al., ”Novel Protection Systems for Microgrids”,2009.

[7] R. J. Best, et al., ”Communication assisted protection selectiv-ity for reconfigurable and islanded power networks”, in Univer-sities Power Engineering Conference (UPEC), 2009 Proceed-ings of the 44th International, 2009, pp. 1-5.

[8] S.G.Mallat, A theory for multi resolution signal decomposi-tion: The wavelet representation, IEEE Trans. Pattern Anal.Machine Intell , vol.11, pp.674-693,July 1989.

[9] T. B. Littler and d. J. Morrow. ”Wavelets for the Analysis andCompression of Power System Disturbances”, IEEE Transac-tions on Power Delivery vol. 14, pp. 358-364, Apr. 1999.

[10] A. S. Oshaba and E. S. Ali, Bacteria Foraging: A New Tech-nique for Speed Control of DC Series Motor Supplied by Photo-voltaic System, International Journal of WSEAS Transactionson Power Systems, Vol. 9, 2014, pp. 185-195.

13


563

[11] A. S. Oshaba, and E. S. Ali, Speed Control of Induction Mo-tor Fed from Wind Turbine via Particle Swarm OptimizationBased PI Controller, Research Journal of Applied Sciences, En-gineering and Technology (Maxwell Science Publication), Vol.5, No. 18, May 2013, pp. 4594-4606.

[12] S. Ustun, C. Ozansoy and A. Zayegh, A Microgrid Protec-tion System with Central Protection Unit and Exensive Com-munication, Proceedings of the 10th International Conferenceon Environment and Electrical Engineering, Rome, 8-11 May2011, pp. 1-4.

[13] X.-P. WANG, Y. Li and Y.-Y. YU, Research on the Relay Pro-tection System for a Small Laboratory Scale Microgrid Sys-tem, Proceedings of the 6th IEEE Conference on IndustrialElectronics and Applications, Beijing, 21-23 June 2011, pp.2712-2716.

[14] P.Makming, S.Bunjongjit, A.Kunakorn, S.Jiriwibhakorn,M.Kando, Fault Diagnosis in Transmission Lines UsingWavelet Transform Analysis, IEEE/PES Transmission andDistribution Conference and Exhibition.

14


564

transmission line protection scheme in presence of · 2018. 11. 26. · key words : islanded...

Documents