High impedance fault (HIF) is a long standing problem which known by very complex phenomena, because of its distinctive characteristics asymmetry and nonlinearity behavior. Besides that, arc is the most serious problem which is mostly associated with high impedance fault, this arc is considered as a source of human life risks and fire hazardous, and additionally result in property damage. From the point of few, detection and discrimination of high impedance fault still remain challenging of protection engineers. In this paper new of high impedance model is introduced and combination of wavelet transform and neural network is presented to detect high impedance fault. Discrete wavelet transform (DWT) is used as feature extraction which extracts useful information from distorted current signal that generated from transmission system network under effect of high impedance fault. In order to improve training convergence and to reduce the number of input to neural network, coefficients of wavelet is calculated and used as input for training back propagation neural network. Multi-layer back propagation neural network (BP-NN) is used as classifier for high impedance fault and discriminate it from such events like capacitor switching and load switching etc.
Comments: 18 Pages.
[v1] 2012-08-19 02:16:22
Unique-IP document downloads: 377 times
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