Fault Diagnosis Method of PMSM Based on Adaptive Dynamic Cat Swarm Optimization of SVM

doi:10.16182/j.issn1004731x.joss.201711038

Abstract

Abstract: In order to solve the problems of common inter-turn short circuit faults of permanent magnet synchronous motor (PMSM), a corresponding motor fault model based on the existing basis of PMSM is established. The eigenvector is extracted by energy spectrum analysis. The penalty factor and RBF-kernel parameter of SVM are optimized by adaptive dynamic cat swarm optimization (ADACSO) algorithm. The optimized SVM is adopted to motor fault diagnosis. The eigenvector obtained by energy spectrum analysis is taken as sample data to conduct simulation experiment. The experiment results indicate that, compared with other optimization algorithms, using ADACSO to optimize SVM parameters can improve the accuracy of SVM in fault diagnosis of PMSM.

Key words: permanent magnet synchronous motor, inter-turn short circuit fault, support vector machine, energy frequency spectrum, adaptive dynamic cat swarm optimization

CLC Number:

TP18
TM341

Wang Yan, Wang Xin, Ji Zhicheng, Yan Dahu. Fault Diagnosis Method of PMSM Based on Adaptive Dynamic Cat Swarm Optimization of SVM[J]. Journal of System Simulation, 2017, 29(11): 2881-2889.

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