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所属单位：自动化学院

发表刊物：METROLOGY AND MEASUREMENT SYSTEMS

关键字：power electronics fault diagnosis wavelet transforms support vector machines directed acyclic graph nearest neighbours

摘要：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.

ISSN号：0860-8229

是否译文：否

发表时间：2017-12-01

合写作者：Shi, Ge,龚春英

通讯作者：崔江