Partial Discharge Fault Identification by Using Adaptive BP Neural Network Based on Second Generation Wavelet

DENG Yurong1; GUO Lijuan1; GUO Feifei2; ZHANG Wei1

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Electric Power Construction ›› 2013, Vol. 34 ›› Issue (6) : 87-91.

Partial Discharge Fault Identification by Using Adaptive BP Neural Network Based on Second Generation Wavelet

DENG Yurong，GUO Lijuan，GUO Feifei，ZHANG Wei

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Abstract

Second generation wavelet (SGWT) and adaptive BP neural network were combined to classify partial discharge fault. Partial discharges (PD) signal was recognized based on SGWT and information entropy theory. Wavelet energy entropy and coefficient entropy were taken as characteristic quantity, and input into neural network for training. In the training process, the neural network could adaptively adjust error to obtain the optimal training network by using the improved conjugate gradient methods. Finally, the comparison between the proposed algorithm, classic neural network and wavelet neural network was carried out on the recognition test of three kings of PDs caused by discharge model, whose results showed that the recognition accuracy and execution efficiency of the proposed algorithm were better that those of classic neural network and wavelet neural network.

Key words

second generation wavelet (SGWT) / neural network / partial discharge / wavelet energy entropy / coefficient entropy / conjugate gradient

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DENG Yurong1，GUO Lijuan1，GUO Feifei2，ZHANG Wei1. Partial Discharge Fault Identification by Using Adaptive BP Neural Network Based on Second Generation Wavelet[J]. Electric Power Construction. 2013, 34(6): 87-91

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