首页> 外文期刊>The Open Cybernetics & Systemics Journal >A New Fault Diagnosis Method for High Voltage Circuit Breakers Basedon Wavelet Packet and Radical Basis Function Neural Network
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A New Fault Diagnosis Method for High Voltage Circuit Breakers Basedon Wavelet Packet and Radical Basis Function Neural Network

机译:基于小波包和径向基函数神经网络的高压断路器故障诊断新方法

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摘要

A new method that researching fault diagnosis of high-voltage (HV) circuit breaker (CB) is proposed. Themethod combines Wavelet Packet (WP) with Radical Basis Function (RBF) Neural Network (NN). Firstly, by applyingthe theory of WP decomposition and reconstruction, the mechanical vibration signal of CB was decomposed into differentfrequency bands, and the coefficients are reconstructed in the corresponding node. After that, the feature vector was extractedby equal-energy segment entropy from reconstructed signals. Finally, fault diagnosis has been realized through theclassification of feature parameters combined with RBF neural network. The experiment outputs show that the methodcan be applied in diagnosis.
机译:提出了一种研究高压断路器(CB)故障诊断的新方法。该方法将小波包(WP)与径向基函数(RBF)神经网络(NN)结合在一起。首先,运用WP分解和重构理论,将CB的机械振动信号分解为不同的频带,并在相应的节点上重构系数。之后,通过等能量分段熵从重构信号中提取特征向量。最后,结合RBF神经网络对特征参数进行分类,实现了故障诊断。实验结果表明该方法可用于诊断。

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