基于相空间重构的电磁继电器电性能参数预测研究

提出了基于相空间重构的电磁继电器电性能参数预测的GA神经网络方法。借助相空间重构技术,重构了电性能参数序列的高维空间轨迹;通过采用基于GA算法的神经网络拟合重构轨迹,建立了电性能参数序列预测模型。以电磁继电器接触电阻和燃弧能量性能参数为例,分析了重构参数对重构轨迹、预测结果的影响。该方法能从高维空间揭示电性能参数退化过程,充分利用神经网络泛化性能,因而预测精度高。实验结果表明,所提方法可行、有效。

Electrical performance parameters prediction of electromagnetic relay based on phase space reconstruction

The GA neural network method based on the phase space reconstruction is proposed for electrical performance parameters prediction for the electromagnetic relays. This paper presents the phase space reconstruction method for the electrical performance parameter series of the electromagnetic relays by using the contanct resistance parameter and arc energy, and then builds the neural network prediction model of the electrical performance parameter series using genetic algorithm. The optimal prediction model parameters are given out by means of analyzing the parameter impacts on the prediction effects. The proposed method has the ability to discover the degradation process of electrical performances for the electromagentic relay in the high dimensional phase space and obtains the better precision of prediction. The experimental results show that the proposed method is feasible and effective.