High impedance fault detection in distribution networks using support vector machines based on wavelet transform

In this paper a new pattern recognition based algorithm is presented to detect high impedance fault (HIF) in distribution networks. In this method, using wavelet transform (WT), the time-frequency based features of the current waveform up to 6.25 kHz are calculated. To extract the best feature set of the generated time frequency features, two methods including principle component analysis (PCA) and linear discriminant analysis (LDA) are used and then support vector machines (SVM) is used as a classifier to distinguish the HIFs considering with and without broken conductor from other similar phenomena such as capacitor banks switching, no load transformer switching, load switching and harmonic loads considering induction motors, arc furnaces. The results show high accuracy of the proposed method in the detection task.

[1]  M. Kizilcay,et al.  Digital simulation of fault arcs in power systems , 2007 .

[2]  L. L. Lai,et al.  Application of discrete wavelet transform to high impedance fault identification , 1998, Proceedings of EMPD '98. 1998 International Conference on Energy Management and Power Delivery (Cat. No.98EX137).

[3]  J. Ruiz,et al.  Study of high impedance fault detection in Levante area in Spain , 2000, Ninth International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.00EX441).

[4]  Shi-Lin Chen,et al.  Energy variance criterion and threshold tuning scheme for high impedance fault detection , 1999, IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233).

[5]  Sang-Hee Kang,et al.  New ANN-Based Algorithms for Detecting HIFs in Multigrounded MV Networks , 2008, IEEE Transactions on Power Delivery.

[6]  Bernhard Schölkopf,et al.  Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[7]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[8]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[9]  O.P. Malik,et al.  High impedance fault detection based on wavelet transform and statistical pattern recognition , 2005, IEEE Transactions on Power Delivery.

[10]  Wook Hyun Kwon,et al.  High impedance fault detection utilizing incremental variance of normalized even order harmonic power , 1991 .

[11]  Sang-Hee Kang,et al.  High-impedance fault detection in distribution networks with use of wavelet-based algorithm , 2006, IEEE Transactions on Power Delivery.

[12]  David G. Stork,et al.  Pattern Classification , 1973 .

[13]  N.I. Elkalashy,et al.  Modeling and experimental verification of high impedance arcing fault in medium voltage networks , 2007, IEEE Transactions on Dielectrics and Electrical Insulation.

[14]  D. Sutanto,et al.  High-impedance fault detection using discrete wavelet transform and frequency range and RMS conversion , 2005, IEEE Transactions on Power Delivery.

[15]  B. Don Russell,et al.  Distribution High Impedance Fault Detection Utilizing High Frequency Current Components , 1982, IEEE Power Engineering Review.

[16]  John A. Orr,et al.  High impedance fault arcing on sandy soil in 15 kV distribution feeders: contributions to the evaluation of the low frequency spectrum , 1990 .

[17]  B. D. Russell,et al.  An arcing fault detection technique using low frequency current components-performance evaluation using recorded field data , 1988 .

[18]  Bijaya Ketan Panigrahi,et al.  High impedance fault detection in power distribution networks using time-frequency transform and probabilistic neural network , 2008 .

[19]  Adly A. Girgis,et al.  A new time domain voltage source model for an arc furnace using EMTP , 1996 .

[20]  O. P. Malik,et al.  Development of a fuzzy inference system based on genetic algorithm for high-impedance fault detection , 2006 .

[21]  S.M. Rovnyak,et al.  Decision tree-based methodology for high impedance fault detection , 2004, IEEE Transactions on Power Delivery.

[22]  Arash Etemadi,et al.  High-impedance fault detection using multi-resolution signal decomposition and adaptive neural fuzzy inference system , 2008 .