Abstract：AC contactor is widely used in electrical systems, and the prediction of its residual electrical life is essential for improving the reliability of these systems. A neural network model is established to predict the residual life of AC contactor, and a method is proposed especially to determine the structure parameters of the model. The accumulation of arc energy and the making time are selected as inputs of the model by the method of Mean Impact Value (MIV), which are two main factors affecting the electrical life of the contactor. Among the neural network models analyzed in this paper, BP neural network model optimized by adaptive genetic algorithm (AGA-BP) has the highest prediction accuracy. The prediction errors of the models with unprocessed inputs, inputs processed by factor analysis and inputs processed by MIV are analyzed. The results show that MIV is suitable for predicting the electrical life of AC contactor. The test data of each sample is used as training sample and testing sample, and the maximum prediction error is less than 11%, so the model is acceptable in actual use.
李奎,李晓倍,郑淑梅,贺建超,武一. 基于BP神经网络的交流接触器剩余电寿命预测[J]. 电工技术学报, 2017, 32(15): 120-127.
Li Kui, Li Xiaobei, Zheng Shumei, He Jianchao, Wu Yi. Residual Electrical Life Prediction for AC Contactor Based on BP Neural Network. Transactions of China Electrotechnical Society, 2017, 32(15): 120-127.
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2017, Vol. 32 
