A novel fault diagnosis method for circuit breakers based on optimized affinity propagation clustering

Online condition monitoring and fault diagnosis of circuit breakers (CBs) is a significant method to effectively improve the stability and reliability of the power system. However, the currently used fault diagnosis method still have certain defects including the inability to identify unknown faults for training samples. Therefore, this paper proposes an evolving method for fast and accurate online fault diagnosis of CBs. On the basis of collecting samples of CB trip/close coil current (CC) features, an optimized affinity propagation (AP) clustering algorithm to accurately extract the sample clustering exemplars is presented. Additionally, operating state identification and fault diagnosis of CBs is carried out by calculating the similarity coefficient between the new sample and exemplars online. Diagnosis of unknown faults is also achieved by introducing the threshold and comparing it with similarity coefficient results. Simulation results prove that the proposed method can precisely identify various known CBs faults and has the ability to recognizes unknown CBs fault samples even when the number of training samples is small, providing a foundation for CB fault location and condition-based maintenance.