Fault Diagnosis Method in Controlled Rectifier Based on Support Vector Machines

Abstract

This paper presents a novel method of applying support vector machines (SVM) to fault diagnosis for controlled rectifier circuits. By keeping the relations between faults and waveforms in a support vector machines model, the support vector machines model can be trained to detect faults and realize automation of fault diagnosis. Three-phase bridge rectifier circuit fault is presented as an example, coupled with the results of a certain power electronic circuit experiment, indicates that the method can accurately diagnose and locate fault for controlled rectifier circuits.