Fault Diagnosis of High Voltage Circuit Breaker Based on Multi-classification Relevance Vector Machine

Abstract

The high voltage circuit breaker’s fault as an important form of electrical contact fault in the power system, which is extremely difficult to diagnose under the condition of small fault dataset. This paper proposes a fault diagnosis method based on multi-classification relevance vector machine for high voltage circuit breakers. To make up with the scarcity of the sample fault data in classifying the features of the high voltage circuit breakers, a multi-classification relevance vector machine algorithm is designed on the basis of “One-Against-One” multi-classification model, and tested by public data-sets to verify the good generating performance of this algorithm on small sample data-sets. Then, the time and the currents features are extracted from the closing coil current information of the high voltage circuit breaker to form fault eigenvector. Consequently, numerous two-classification relevance vector machine models were trained and then tested for the optimality of the acquired parameters. The results show that the proposed algorithm can effectively identify many faults of circuit breaker and has better classification accuracy than BP neural network and Support Vector Machine under conditions of small sample data.