Fault diagnosis of high-voltage circuit breakers using wavelet packet technique and support vector machine

Abstract

The subject addressed is mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis. The vibration signature measured from the SF6 HVCB is processed using the fast Fourier transform (FFT) and the wavelet packet technique (WPT). With the FFT and the WPT, the vibration signature is decomposed into different frequency bands, and the energy of the signal is extracted from individual frequency bands and calculated as the individual condition eigenvector. Followed by that, parameters captured from the WPT are optimised with the particle swarm optimisation and the result is used to train a multi-fault classification model based on the kernel method of the support vector machine (SVM). At the end, fitness values are evaluated with the trained SVM to classify conditions and diagnose faults of the SF6 HVCB.