Abstract
Mechanical faults of high voltage circuit breakers (HVCBs) are one of the most important factors that affect the reliability of power system operation. Because of the limitation of a lack of samples of each fault type; some fault conditions can be recognized as a normal condition. The fault diagnosis results of HVCBs seriously affect the operation reliability of the entire power system. In order to improve the fault diagnosis accuracy of HVCBs; a method for mechanical fault diagnosis of HVCBs based on wavelet time-frequency entropy (WTFE) and one-class support vector machine (OCSVM) is proposed. In this method; the S-transform (ST) is proposed to analyze the energy time-frequency distribution of HVCBs’ vibration signals. Then; WTFE is selected as the feature vector that reflects the information characteristics of vibration signals in the time and frequency domains. OCSVM is used for judging whether a mechanical fault of HVCBs has occurred or not. In order to improve the fault detection accuracy; a particle swarm optimization (PSO) algorithm is employed to optimize the parameters of OCSVM; including the window width of the kernel function and error limit. If the mechanical fault is confirmed; a support vector machine (SVM)-based classifier will be used to recognize the fault type. The experiments carried on a real SF6 HVCB demonstrated the improved effectiveness of the new approach.
Highlights
High voltage circuit breakers (HVCBs) play an important role in the protection and control of power systems
This paper presents a new ST and one-class support vector machine (OCSVM)-based approach for HVCBs mechanical fault diagnosis
Several types of wavelet entropy are defined based on different principles or processing methods, such as wavelet energy entropy (WEE), wavelet time entropy (WTE), wavelet singular entropy (WSE) and wavelet time-frequency entropy (WTFE) [15]
Summary
High voltage circuit breakers (HVCBs) play an important role in the protection and control of power systems. The existing vibration signal processing methods such as short-time energy [7], dynamic time warping (DTW) [8], wavelet packet transform (WPT) [9] and empirical mode decomposition (EMD) [10,11] have all achieved good results in this area. Neural networks (NNs) [9] and SVM [10,11] have made a significant contribution to fault recognition of HVCBs. Because HVCBs generally operate infrequently, it is quite difficult to get enough vibration samples of different types of HVCB mechanical faults for training multi-class classifiers. Multi-class classification methods such as NNs which rely on lots of training samples are not appropriate for analyzing the mechanical status and identifying HVCB faults. Three different types of faults are simulated in a field experiment on a real HVCB to verify the validity of the new method
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