New fault diagnosis of circuit breakers

被引：66
|
作者：
Lee, DSS [1 ]
Lithgow, BJ [1 ]
Morrison, RE [1 ]
机构：
[1] Monash Univ, Dept Elect & Comp Syst Engn, Melbourne, Vic 3800, Australia
来源：
IEEE TRANSACTIONS ON POWER DELIVERY | 2003年 / 18卷 / 02期
关键词：
circuit breakers; monitoring; neural networks; transient analysis; vibrations; wavelet transforms;
D O I：
10.1109/TPWRD.2003.809615
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Wavelet packets and neural networks have been used to analyze the vibration data of circuit breakers (CBs) for the detection of incipient CB faults. Wavelet packets are used to convert measured vibration data from healthy and defective CBs into wavelet features. Selected features highlighting the differences between healthy and faulty condition are processed by a back-propagation neural network for classification. Testing has been done for three 66-kV CBs with simulated faults. Detection accuracy is shown to be far better than other classical techniques such as the windowed Fourier transform, stand alone artificial neural networks or expert system. The accuracy of detection for some faults can be as high as 100%.
引用
收藏
页码：454 / 459
页数：6
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