A FAST CLASSIFICATION METHOD OF FAULTS IN POWER ELECTRONIC CIRCUITS BASED ON SUPPORT VECTOR MACHINES

作者：Cui, Jiang^*; Shi, Ge; Gong, Chunying

来源：Metrology and Measurement Systems, 2017, 24(4): 701-720.

DOI：10.1515/mms-2017-0056

• power electronics
• fault diagnosis
• wavelet transforms
• support vector machines
• directed acyclic graph
• nearest neighbours

Fault detection and location are important and front-end tasks in assuring the reliability of power electronic circuits. In essence, both tasks can be considered as the classification problem. This paper presents a fast fault classification method for power electronic circuits by using the support vector machine (SVM) as a classifier and the wavelet transform as a feature extraction technique. Using one-against-rest SVM and one-against-one SVM are two general approaches to fault classification in power electronic circuits. However, these methods have a high computational complexity, therefore in this design we employ a directed acyclic graph (DAG) SVM to implement the fault classification. The DAG SVM is close to the one-against-one SVM regarding its classification performance, but it is much faster. Moreover, in the presented approach, the DAG SVM is improved by introducing the method of K-nearest neighbours to reduce some computations, so that the classification time can be further reduced. A rectifier and an inverter are demonstrated to prove effectiveness of the presented design.

• 出版日期2017-12
• 单位南京航空航天大学