Implementation of Neural Network for High Impedance Fault Detection

Implementation of Neural Network for High Impedance Fault Detection

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Author(s)

Author(s): Dhiraj Ahuja

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699 1088 45-77 Volume 1 - Dec 2012

Abstract

In the thesis the aim was to detect the high impedance fault occurring on radial distribution system using neural network. A multilayer perceptron was used for distinguishing the linear and nonlinear high impedance faults by taking the feature vector as input R.M.S value of third and fifth harmonic components of feeder voltage and feeder current were used as a feature vector obtained by applying the fast Fourier Transformation on the feeder voltage and feeder current.The values of feeder voltage and feeder current are obtained for two kinds of fault cases (i.e. linear and nonlinier) by simulating the model of high impedance fault system. The values of third and fifth harmonics were obtained by applying the Fast Fourier Transformation .RMS values of these harmonics were used to train the Multilayer Perceptron Neural Network for classification of these two type of faults. It consists of total three layers, two hidden layers and one output layer. Each hidden layer consists of four neurons and one output layer consists of two neuron. This network was trained by using the Back propagation algorithm .Many types of back propagation algorithms were tested and it’s found that trainlm and trainbr were classifying the two kinds of fault s more perfectly compared to other algorithms. As well as for selecting the no. of neurons the network is tested for different number of neuron in each layer and it’s found that the network consisting of four neuron in each hidden layer performing well. The network was tested for different transfer function and it was found that it’s performance is good when log-sigmoid transfer function is used in all three layers or when tan-sigmoid transfer function is used by the neuron in two hidden layer and linear transfer function is used by neuron in output layer.

References

  1. B.M Aucoin, B.Don Russell,” Distribution High Impedance Fault Detection Utilizing High Frequency Current Component “IEEE Trans. On PAS,Vol.PAS-101,No.6 June 1982.pp.1596-1606
  2. Emanuel,A.E.,Cyganski,D.,Orr,J.A,Gulachenski,E.M.,”High Impedance Fault Arcing on Sandy Soil in 15kv Distribution Feeders:Contribution to the evaluation of the low Frequency Spectrum,”IEEE Transaction On Power Delivery,Volume 5,issue 2,April 1990 pp676-686
  3. Aucoin B,Michel, and Jones,Robert H.,”High impedance Fault Detection Implementation Issues”,IEEE Transaction on power Delivery,Vol.11,No.1 January,1996,pp.139-148
  4. A.M Sharaf, L.A. Snider, K.Debnath,” A Third Harmonic Sequence ANN Based Detection Scheme For High Impedance Faults”,Proceedings of the ISEDEM Singapore ,pp.802-806
  5. L.A Snider ,Yuen Yee Shan,”The Artificial Neural Networks based Relay Algorithm Distribution System High Impedance Fault Detection”,Proceedings of the fourth International Conference on Advances in Power Sytem Control ,Operation and Management,APSCOM-97,Hong Kong,November 1997,pp 100-106
  6. L.A. Snider,Yuen yee Shan” The Artificial Neural Networks based Relay Algorithm For the Detection Of High Impedance Faults”,ELSEVIER Transactions on Neuro Computing ,1988,pp 243-254
  7. T.M. Li Snider ,L.A. Snider. Lo,C.H. Cheung and K.W.Chan “High Impedance fault detection Using Artificial Neural Network”, Proceedings of the fourth International Conference on Advances in power System Control , Operation and Management,APSCOM ,Hong Kong, November 2003,pp 821-826
  8. A.M Sharaf ,L.A Snider,K,Debnath ,” A Neural Network based Back error Propagation Relay Algorithm for Distribution System High Impedance fault Detection”, Proceeding the ISEDEM. Singapore,pp.613-20
  9. Adel M.Sharaf,Guosheng Wang,”High Impedance Fault Detection using Low Order Pattern Harmonic Detection”,IEEE TRANS.pp 883-886.
  10. 10.Adel M.Sharaf,Guosheng Wang,”High Impedance Fault Detection using Feature Pattern Based Relaying”IEEE trans.pp 222-226
  11. A.M,R M. EI-Sharkawy,H.E.A,Talaat,M.A.L.Badr”Novel Alpha-Transformation Distance Relaying Scheme”.IEEE trans.pp 754-757
  12. A.M.Sharaf, L.A. Snider, K.Debnath,” A Neural Network Based relaying Scheme For Distribution System High Impedance Fault Detection “,IEEE trans.pp 321-326
  13. T.M Lai Snider,L.A.Snder.E.Lo”Wavelet transform based algorithm for the detection of stochastic high impedance faults”,ELSEVIER Transaction on Electric Power System Research,pp 626-633
  14. Abhishek Bansal and G.N.pillai “High Impedance Fault Detection Using Artificial Neural Networks”,trans.pp 148-152
  15. M.M,Eissa (SMIEEE) G.MA. Sowilam,A.M Sharaf (SMIEEE)” Anew Protection Detection Technique For High Impedance Fault Using Neural Network” IEEE trans .pp 146-151
  16. Howard Demuth ,Mark Beale Artificial Neural Network Matlab User guide
  17. Back Propagation Learning Algorithm ,www.wikipedia.org
  18. A.A . Girgas, W. Chang, and E.B.Makram,”Analysis of high impedance fault generated signals using a Kalman Filtering Approach,”IEEE Trans.Power Deliv.5(October(4))(1990) pp.1714-1724

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International Journal of Sciences is Open Access Journal.
This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Author(s) retain the copyrights of this article, though, publication rights are with Alkhaer Publications.

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