采用HHT振动分析的低压断路器合闸同期辨识

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Abstract A method that effectively de-noising vibration signal and extracting fault feature is necessary when diagnose the switching synchronism fault of low voltage circuit breaker(LVCB). A vibration signal analysis method based on Hilbert-Huang transform(HHT) is put forward,in which intrinsic mode function(IMF) components are extracted by empirical mode decomposition(EMD) to reflect local characteristic of LVCB vibration signal,and top five of IMF components energy not only is representation of vibration feature,but also has de-noising work. By analyzing to LVCB vibration signal in time-domain,kurtosis and mean square value are used as auxiliary mechanic characteristic index. With feature vector of energy ratio of front five IMF components,kurtosis and mean square value,the neural network based on particle swarm optimization(PSO) and radial basis function(RBF) is expounded to model fault recognition of asynchronous switching for LVCB. By results of experiment and simulation,it is effective to analysis switching synchronism with intelligent technology of comprehensive such as time-domain analyze,EMD and PSO-RBF neural network,which is a new diagnosis method for circuit breaker in special for three phases switching synchronism vibration of LVCB.

Key words： Low voltage circuit breaker vibration analysis switching synchronism empirical mode decomposition IMF component energy ratio neural networks fault identification

Received: 08 October 2013 Published: 22 January 2015

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TM561

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	Miao Xiren
	Wu Xiaomei
	Shi Dunyi
	Guo Moufa
	Wang Wuyu

Cite this article:

Miao Xiren,Wu Xiaomei,Shi Dunyi等. Switching Synchronism Identification of Low Voltage Circuit Breaker Utilizing HHT Analysis to Vibration Signal[J]. Transactions of China Electrotechnical Society, 2014, 29(11): 154-161.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I11/154

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