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Research on optimal identification method of circuit breaker defect type based on phase space reconstruction and SVM
Author(s) -
Zhao Shutao,
Ma Li,
Wang Kedeng,
Wang Erxu,
Xiao Yan
Publication year - 2019
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22965
Subject(s) - circuit breaker , support vector machine , singular value decomposition , vibration , singular value , chaotic , aliasing , algorithm , control theory (sociology) , signal reconstruction , signal (programming language) , three phase , computer science , engineering , signal processing , electronic engineering , artificial intelligence , filter (signal processing) , acoustics , voltage , eigenvalues and eigenvectors , digital signal processing , physics , electrical engineering , programming language , control (management) , quantum mechanics
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.