The Open Cybernetics & Systemics Journal

2014, 8 : 410-417
Published online 2014 December 31. DOI: 10.2174/1874110X01408010410
Publisher ID: TOCSJ-8-410

A New Fault Diagnosis Method for High Voltage Circuit Breakers Based on Wavelet Packet and Radical Basis Function Neural Network

Liu Mingliang , Wang Keqi , Sun Laijun and Zhang Jianfeng
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, P.R. China.

ABSTRACT

A new method that researching fault diagnosis of high-voltage (HV) circuit breaker (CB) is proposed. The method combines Wavelet Packet (WP) with Radical Basis Function (RBF) Neural Network (NN). Firstly, by applying the theory of WP decomposition and reconstruction, the mechanical vibration signal of CB was decomposed into different frequency bands, and the coefficients are reconstructed in the corresponding node. After that, the feature vector was extracted by equal-energy segment entropy from reconstructed signals. Finally, fault diagnosis has been realized through the classification of feature parameters combined with RBF neural network. The experiment outputs show that the method can be applied in diagnosis.

Keywords:

Characteristic entropy, equal-energy segment, fault diagnosis, RBF Neural Network, wavelet packet.