Fault Diagnosis Model of Circuit Breaker Based on Sparse Representation and M-ELM

doi:10.16182/j.issn1004731x.joss.201711032

Abstract

Abstract: In order to improve the deficiencies of the conventional methods used to evaluate the mechanical properties of circuit breaker, a new circuit breaker diagnosis model based on sparse representation and M-ELM (Memetic-Extreme Learning Machine) is constructed. Auxiliary mark motion on the pull rod or shaft is recorded by a high speed camera when the circuit breaker is open or close. The motion trajectory is acquired through sparse representation and mechanical parameters, such as open and close velocity, are calculated according to the travel-time curve of the circuit breaker. With mechanical parameters characteristic values being inputs of M-ELM, fault of circuit breaker can be diagnosed. Experiment results on the circuit breaker of 12kV show the effectiveness and superiority of the model.

Key words: circuit breaker, object tracking, sparse representation, multi-feature fusion, M-ELM

CLC Number:

TM76

Niu Weihua, Liang Guishu, Zhao Peng. Fault Diagnosis Model of Circuit Breaker Based on Sparse Representation and M-ELM[J]. Journal of System Simulation, 2017, 29(11): 2828-2839.

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