Fault data compression of power system with wavelet neural network based on wavelet entropy

Authors:
Zhigang Liu;Dabo Zhang
Affiliations:
Institute of Electrification & Automation, Southwest Jiaotong University, Chengdu, Sichuan, China;Institute of Electrification & Automation, Southwest Jiaotong University, Chengdu, Sichuan, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

Through the analysis of function approximation with wavelet transformation, an adaptive wavelet neural network is introduced in the paper, which is applied in data compression of fault data in power system. In addition, the wavelet entropy is adopted to choose the hidden nodes in the wavelet neural network. The learning algorithm of the wavelet neural network based on wavelet entropy is proposed and discussed for data compression of fault data in power system. The simulation results show that it is feasible and valid in the end.