DT-CWT based event feature extraction for high impedance faults detection in distribution system

Summary In this paper an algorithm for high impedance fault detection is presented. This algorithm uses dual tree complex wavelet transform to extract the features of disturbance signals according to the post- and pre-disturbance data windows. There are also a frequency tracking unit and a disturbance detection unit in this algorithm for enhancing the resolution of features. A trained probabilistic neural network is used to discriminate between the fault and other events. EMTP-RV has been used for simulation of various events with different conditions for training and testing the algorithm. As this algorithm uses the features extracted from the events, the fault detection can be done with more reliability. Results of implementing the algorithm for high impedance fault detection in a distribution test feeder show a high level of dependability and security. Copyright © 2014 John Wiley & Sons, Ltd.

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

[2]  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 .

[3]  Susmita Kar,et al.  Time-frequency transform-based differential scheme for microgrid protection , 2014 .

[4]  Feng Li,et al.  High Impedance Fault Location in Distribution System Based on Nonlinear Frequency Analysis , 2014 .

[5]  Pradipta Kishore Dash,et al.  High impedance fault detection in distribution feeders using extended kalman filter and support vector machine , 2010 .

[6]  Mostafa Sarlak,et al.  Design and implementation of a systematically tunable high impedance fault relay. , 2010, ISA transactions.

[7]  D. F. Specht,et al.  Probabilistic neural networks for classification, mapping, or associative memory , 1988, IEEE 1988 International Conference on Neural Networks.

[8]  Nick G. Kingsbury,et al.  The dual-tree complex wavelet transform: A new efficient tool for image restoration and enhancement , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[9]  Hamzah Arof,et al.  High impedance fault location in 11 kV underground distribution systems using wavelet transforms , 2014 .

[10]  M. Sarlak,et al.  High impedance fault detection using combination of multi-layer perceptron neural networks based on multi-resolution morphological gradient features of current waveform , 2011 .

[11]  B. M. Aucoin,et al.  A microprocessor-based digital feeder monitor with high-impedance fault detection , 1994 .

[12]  C. W. Taylor,et al.  Load representation for dynamic performance analysis , 1993 .

[13]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[14]  Nagy I. Elkalashy,et al.  DWT-Based Extraction of Residual Currents throughout Unearthed MV Networks for Detecting High Impedance Faults due to leaning Trees , 2007 .

[15]  J. L. Guardado,et al.  Modeling and detection of high impedance faults , 2014 .

[16]  S. M. Brahma,et al.  Detection of High Impedance Fault in Power Distribution Systems Using Mathematical Morphology , 2013, IEEE Transactions on Power Systems.

[17]  Zbigniew Leonowicz,et al.  Fault location in power networks with mixed feeders using the complex space-phasor and Hilbert–Huang transform , 2012 .

[18]  Carl L. Benner,et al.  Field experience with high-impedance fault detection relays , 2006, CPRE 2006.