An open circuit fault identification method of inverter based on FDA-KNN

Abstract

The fault of inverters seriously affects individuals' lives, and researchers have become increasingly interested in its method of fault identification. In view of the similarity of three-phase current data of inverter open circuit fault and the low classification accuracy caused by the slight change of three-phase current data measured for distinct open circuit faults, the FDA-KNN method is proposed. Moreover, the method extracts fault characteristics from the three-phase current value of the inverter prior to label discrimination. In the first place, fisher linear discrimination (FDA) is adopted to separate normal data from fault data to extract fault data features, and the subsequently K-nearest neighbor algorithm (KNN) is employed to label faults and identify the open circuit fault type of inverter. The results demonstrate that this method is straightforward to implement and comprehend. It solves the issue of fault classification for similar inverter three-phase current data with greater precision than conventional methods.