Abstract
ABSTRACTDuring the operation process of the high-voltage circuit breaker, the changes of vibration signals reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition) and correlation dimension and a classification method with BP (back propagation) neural network. Firstly, original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, correlation dimension of the top four IMFs by the G–P algorithm is calculated and the characteristic vector of the vibration signal of the circuit breaker is formed. At last, the classification of characteristic parameter is realized with a simple BP neural network for fault diagnosis. The experimentation without loads indicates that the method can easily and accurately diagnose breaker faults and exploit a new road for diagnosis of high-voltage circuit breakers.
Highlights
High-voltage (HV) circuit breaker plays a key role in controlling and protecting the power network
Hung et al [4] used empirical mode decomposition (EMD) to decompose the mechanical vibration signal of high-voltage circuit breaker, but the EMD method has the disadvantage of modal aliasing, and Hu et al [5] has clearly analysed the cause of modal aliasing in EMD
ensemble empirical mode decomposition (EEMD) decomposes the signal into several intrinsic mode functions (IMFs) components from high frequency to low frequency, the IMF components fully embody the details of the original signal
Summary
High-voltage (HV) circuit breaker plays a key role in controlling and protecting the power network. Hung et al [4] used empirical mode decomposition (EMD) to decompose the mechanical vibration signal of high-voltage circuit breaker, but the EMD method has the disadvantage of modal aliasing, and Hu et al [5] has clearly analysed the cause of modal aliasing in EMD. This paper presents a feature extraction method of vibration signal with the combination of fractal theory and ensemble empirical mode decomposition (EEMD) and a classification method with the back propagation (BP) neural network. BP neural network is a network structure based on gradient descent algorithm, unlike the SVM, it can adjust the weights by error BP. At present, it is widely used in the fault diagnosis field. The experiment indicates that the method can and accurately diagnose breaker faults and exploit a new road for diagnosis of HV circuit breakers
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