Detection of High Impedance Faults in Distribution System

This investigation seeks to present a new method of identifying high impedance faults (HIFs) in the distribution feeder. Discrete wavelet transformations (DWT) and neural networks (NN) have been widely used in power system research. Consequently, this work developed a novel method to distinguish effectively the HIFs by integrating DWT with NN. The proposed scheme has two distinct features. First, the input signal of this algorithm is neutral line current, rather than the traditional currents based on three individual phases. Second, HIFs identification applies the details at levels 2, 3 and 4 and the approximations at level 4 of the neutral line current are employed for. The results of staged fault clearly indicate that the proposed can accurately find the HIFs in the distribution feeder

[1]  O. A. S. Youssef,et al.  A wavelet-based technique for discrimination between faults and magnetising inrush currents in transformers , 2002, IEEE Power Engineering Society Summer Meeting,.

[2]  José A. Aguado,et al.  Wavelet-based ANN approach for transmission line protection , 2003 .

[3]  Omar A.S. Youssef.  A wavelet-based technique for discrimination between faults and magnetising inrush currents in transformers , 2002 .

[4]  L. Snider,et al.  The artificial neural networks based relay algorithm for distribution system high impedance fault detection , 1997 .

[5]  B. D. Russell,et al.  A digital signal processing algorithm for detecting arcing faults on power distribution feeders , 1989 .

[6]  B. D. Russell,et al.  Practical High Impedance Fault Detection for Distribution Feeders , 1996, Proceedings of Rural Electric Power Conference.

[7]  Ramesh A. Gopinath,et al.  Wavelets and Wavelet Transforms , 1998 .

[8]  Eduardo D. Sontag,et al.  Feedback Stabilization Using Two-Hidden-Layer Nets , 1991, 1991 American Control Conference.

[9]  Jie Liang,et al.  A wavelet multiresolution analysis approach to fault detection and classification in transmission lines , 1998 .

[10]  L. A. Leturiondo,et al.  A new approach to high impedance fault detection using wavelet packet analysis , 2000, Ninth International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.00EX441).

[11]  N. D. Rao,et al.  Artificial neural network based fault diagnostic system for electric power distribution feeders , 1995 .

[12]  Robert Lee,et al.  Development and Testing of an Electro-Mechanical Relay to Detect Fallen Distribution Conductors , 1982, IEEE Transactions on Power Apparatus and Systems.