基于相空间重构的航空电弧故障识别方法

doi:10.19595/j.cnki.1000-6753.tces.L80275

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Abstract Point contact test, guillotine test and vibration test, which are based on the American standards containing UL 1699 and SAE AS 5692, were finished under the condition of 115V/400Hz aircraft power supply in order to detect the alternating series arc faults in aviation. The normal and the arc fault current signals were extracted using the industrial personal computer and the data acquisition card, then the box counting dimensions, information dimensions, mean ratios and their standard deviations and peak-to-peak values of the current signals were calculated based on phase space reconstruction and wave comparison. The results show that the huge changes occur in the above characteristic quantities compered with the normal situation during the arc fault. The sample of the arc fault which is constructed by the above characteristic quantities are deemed as the inputs of the BP neural network optimized by genetic algorithm. Whether the circuit normal or not is deemed as the outputs of the BP neural network in order that identify the arc fault. The results show that the recognition efficiency of this method is above 96%.

Key words： Phase space reconstruction phase plane box counting dimension information dimension genetic algorithm BP neural network

Received: 29 June 2018 Published: 05 March 2020

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	Cui Ruihua
	Cao Huan

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Cui Ruihua,Cao Huan. An Arc Fault Identification Method in Aircraft Based on Phase Space Reconstruction[J]. Transactions of China Electrotechnical Society, 2020, 35(zk1): 243-250.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L80275 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/Izk1/243

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