HIGH IMPEDENCE FAULT DETECTION IN

DISTRIBUTED SYSTEM UNDER DISTRIBUTED

GENERATION

MAHANTESH CHIKKADESAIELECTRICAL AND ELECTRONICS DEPARTMENTKLS’s VISHWANATHRAO DESHPANDE RURAL INSTITUTE OF TECHNOLOGYHALIYAL.

DATE: 28-04-2015

Presented by:

INTRODUCTION:HIF

Causes for HIFs

Transition conditions

Methods to extract useful information from these high frequency components or harmonics are: 1)Fourier transform 2)Wavelet transform 3)Artificial neural network 4)Fuzzy logic or combination of these

HIF ModellingEmanuel arc model-proposed in 2003 The fault current flows towards the ground if the phase voltage >positive DC voltage Vp,The fault current reverses,if the phase voltage< negative DC voltage Vn,no fault current flows,for values of the phase voltage between Vn&Vp.

DISCRETE WAVELET TRANSFORM Feature extractionTransient voltages and currents during fault carry high frequency component or harmonics which carry important information regarding type and location of fault. The signal of the desired componentNumber of decomposition

Multi-resolution analysisLow frequency signals called approximations

High frequency signals called details

NEURAL NETWORKReliable method Algorithms use the gradient of the performance function to determine weights for better performance.

The gradient is determined using a technique called back propagation which involves performing computations backwards through the network.

The Implementation of Proposed Methodology

Network Simulation

Conditions considered for training patterns data generationAnd Simulated wave forms

FEATURE EXTRACTIONUsed to extract raw fault signals

Outputs of this are inputs for ANN

Magnitude of transient energy of fault signal>non fault signal due to higher frequency

ADVANTAGES:Low cost.Multiple fault locations.Using standard back propagation approach was used to locate fault in a distribution network connected with distributed generators.

DISADVANTAGES:ANN is highly dependent on amount and quality of the well-trained ANN algorithm.Limited amount of information, or inaccurate information, will affect the performance of the algorithmIt has slow convergence ANN algorithm needs to be retrained whenever there are changes in the system

CONCLUSION This paper presents the application of wavelet multi resolution analysis in combination with artificial neural network for accurate classification and locating the fault.

Capabilities of neural network in pattern classification were utilized to classify the faults.

After successful classification details of fault signals are used to locate the fault. Simulation studies were performed for different fault conditions with faults at different phases.

[1] Manohar Singh “high impedance fault detection in distributed system under distributed generation”, The Journal of CPRI, volume10, issue02, june2014, pp 245-252.[2] Sung-Il Jang, Duck-Su Lee “Application of Fault Location Method to Improve Protect-ability for Distributed Generations”, Journal of Electrical Engineering & Technology, Vol. 1, No. 2, pp. 137~144, 2006.[3] M. Begović, A. Pregelj, A. Rohatgi “Impact of Renewable Distributed Generation on Power Systems”, Proceedings of the 34th Hawaii International Conference on System Sciences – 2001.[4] Mamta Patel, R. N. Patel “Fault Detection and Classification on a Transmission Line using Wavelet Multi Resolution Analysis and Neural Network” International Journal of Computer Applications (0975 – 8887) Volume 47– No.22, June 2012.[5] Daqing Hou, Schweitzer Engineering Laboratories, Inc. “Detection of High-Impedance Faults in Power Distribution Systems”.

REFERENCES

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