基于一维卷积神经网络的低压万能式断路器附件故障诊断

doi:10.19595/j.cnki.1000-6753.tces.190453

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Abstract Since AC power supply is adopted in the coil circuit of switching accessories for low voltage conventional circuit breaker, the randomness of the closing phase angle of the coil circuit may cause the difference of current signals under the same operating state. Using the traditional intelligent fault diagnosis method may lead to inaccurate fault feature extraction of current signal, and then result in lower fault identification rate. To solve this problem, an intelligent fault diagnosis algorithm based on adaptive one-dimensional deep convolutional neural network with wide first-layer kernel (AW-1DCNN) is proposed. Compared with the traditional intelligent fault diagnosis method including two stages of manual feature extraction and fault classification, the proposed method combines these two stages into one. Firstly, considering the characteristics of the current signal of the switching coil, a one-dimensional convolutional neural network model is adopted, and the convolution kernel for the first convolutional layer of the model is set as a wide convolution kernel to expand the receptive region. Secondly, the feature extraction layer is used to complete the adaptive feature extraction of the current signal. Finally, the fault diagnosis results are output by the Softmax classifier. The experimental results demonstrate that the proposed algorithm can not only effectively identify the same fault at different phase angles, but also maintain a high fault identification rate in the generalization experiment, which effectively overcomes the influence of closing phase angle on fault diagnosis results.

Key words： Conventional circuit breaker switching accessories coil current one-dimensional deep convolutional neural network fault diagnosis

Received: 22 April 2019

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TM561

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	Sun Shuguang
	Li Qin
	Du Taihang
	Cui Jingrui
	Wang Jingqin

Cite this article:

Sun Shuguang,Li Qin,Du Taihang等. Fault Diagnosis of Accessories for the Low Voltage Conventional Circuit Breaker Based on One-Dimensional Convolutional Neural Network[J]. Transactions of China Electrotechnical Society, 2020, 35(12): 2562-2573.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.190453 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I12/2562

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