Abstract
Wavelet packets and neural networks have been used to analyze the vibration data of circuit breakers for the detection of incipient circuit breaker faults. Wavelet packets are used to convert measured vibration data from healthy and defective circuit breakers 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 circuit breakers 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%.
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