Research on optimal identification method of circuit breaker defect type based on phase space reconstruction and SVM

• Published in: IEEJ transactions on electrical and electronic engineering Vol. 14; no. 10; pp. 1471 - 1477
• Main Authors: Zhao, Shutao, Ma, Li, Wang, Kedeng, Wang, Erxu, Xiao, Yan
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2019; Wiley Subscription Services, Inc
• Subjects: Aliasing; Circuit breakers; Delay time; Diagnostic systems; Genetic algorithms; Mechanical components; Parameter identification; phase space reconstruction; Reconstruction; Signal processing; Singular value decomposition; Support vector machines; SVD; SVEE; SVM; SVOE; Vibration
• ISSN: 1931-4973; 1931-4981
• DOI: 10.1002/tee.22965

The vibration signals generated by the transmission and impact of circuit breaker mechanical components have chaotic characteristics and using conventional signal processing method is difficult to distinguish the abnormal operation process quickly and accurately. Based on the mutual information method and the Cao algorithm, the delay time and the embedding dimension of the phase space reconstruction parameters are calculated and optimized according to the chaotic characteristics of the vibration signals. The singular value order energy (SVOE) and the singular value energy entropy (SVEE) are obtained by decomposing the phase space reconstruction matrix, which is determined by the optimal phase space reconstruction parameters, and the support vector machines (SVM) are used to identify the states of the circuit breaker in operation. The experimental results show that the combination phase space reconstruction and singular value decomposition (PSR‐SVD) can accurately extract the characteristics of the vibration signal of the circuit breaker, and the genetic algorithm (GA)‐improved SVM can quickly and effectively identify the circuit breaker defect types, which solves the problems of path distortions, energy leaks, modal aliasing, and lack of samples in existing diagnostic methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.