Multi-mapping fault diagnosis of high voltage circuit breaker based on mathematical morphology and wavelet entropy

Abstract

Mechanical faults of high voltage circuit breakers (CBs) seriously affect the reliability of their operation, which may cause severe damage to power systems. In order to monitor operational conditions and detect mechanical faults of CBs, a multi-parameter monitoring system is designed and a fault diagnosis method based on multi-mapping is proposed. The paper focuses on the trip/close circuits, the spring-charging mechanism and the transmission mechanism, and obtains four current signals and a vibration signal that can reflect CB conditions. For the current signals, a morphological filter is used to remove noise and then characteristics of the waveforms' shape information are extracted. For vibration signals, the wavelet packet transform is used to decompose the signal into various frequency bands, and the sample entropy of the low frequency bands and the wavelet energy of the high frequency bands are calculated, respectively. Based on these feature parameters, a multi-mapping strategy is proposed for CB fault diagnosis. Laboratory experiments have been conducted to obtain on-site signals under various conditions, and experiment results have verified that monitoring the aforementioned signals and using the corresponding feature extraction and fault diagnosis methods, the mechanical faults of high voltage CBs can be effectively diagnosed.