基于BP神经网络的交流接触器剩余电寿命预测

doi:10.19595/j.cnki.1000-6753.tces.161050

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (2659 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

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.

Key words： AC contactor BP neural network adaptive genetic algorithm life prediction

Received: 07 July 2016 Published: 18 August 2017

PACS:

TM572.2

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Li Kui
	Li Xiaobei
	Zheng Shumei
	He Jianchao
	Wu Yi

Cite this article:

Li Kui,Li Xiaobei,Zheng Shumei等. Residual Electrical Life Prediction for AC Contactor Based on BP Neural Network[J]. Transactions of China Electrotechnical Society, 2017, 32(15): 120-127.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.161050 OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I15/120

[1] 陆俭国, 王景芹. 低压保护电器可靠性理论及其应用[M]. 北京: 机械工业出版社, 2004.
[2] 苏秀苹, 杨怡君, 李俊峰, 等. 基于变异系数-模糊评判法的交流接触器动态响应批不确定度的分析与评价[J]. 电工技术学报, 2016, 31(8): 102-108.
Su Xiuping, Yang Yijun, Li Junfeng, et al. Analysis and evaluation of dynamic response lot uncertainty of AC contactor based on coefficient of variance and fuzzy comprehensive theory[J]. Transactions of China Electrotechnical Society, 2016, 31(8): 102-108.
[3] Zhou Xue, Cui Xinglei, Chen Mo, et al. Evaporation erosion of contacts under static arc by gas dynamics and molten pool simulation[J]. IEEE Transactions on Plasma Science, 2015, 43(12): 1-12.
[4] Yang Xiaocheng, Huang Jiang, Li Zhenbiao, et al. The preceding voltage pulse and separation welding mechanism of electrical contacts[J]. IEEE Transactions on Components Packaging & Manufacturing Technology, 2016, 6(6): 846-853.
[5] Zhao Laijun, Li Zhenbiao, Zhang Hansi, et al. Random occurrence of contact welding in electrical endurance tests[J]. IEICE Transactions on Electronics, 2011, 94(9): 1362-1368.
[6] Li Kui, Lu Jianguo, Wu Yi, et al. Research on the overload protection reliability of moulded case circuit-breakers and its test device[J]. Journal of Zhejiang University Science A, 2007, 8(3): 453-458.
[7] 陈德为, 庄煜祺, 黄航宇. 智能交流接触器三维动态测试与分析[J]. 电工技术学报, 2014, 29(11): 162-168.
Chen Dewei, Zhuang Yuqi, Huang Hangyu. 3D dynamic testing and analysis of intelligent AC contactor[J]. Transactions of China Electrotechnical Society, 2014, 29(11): 162-168.
[8] 林抒毅, 许志红. 交流接触器三维动态过程数值计算与分析[J]. 中国电机工程学报, 2014, 53(18): 518-529.
Lin Shuyi, Xu Zhihong. Simulations and numerical analysis on 3D dynamic process of alternating current contactors[J]. Proceedings of the Chinese Society of Electrical Engineering, 2014, 53(18): 518-529.
[9] 李奎, 陆俭国, 张冠生. 继电器触头接触可靠性预测方法的研究[J]. 电工技术学报, 1999, 14(2): 55-59.
Li Kui, Lu Jianguo, Zhang Guansheng. The approach to predication of contact reliability for relay[J]. Transactions of China Electrotechnical Society, 1999, 14(2): 55-59.
[10] 李志刚, 姚芳, 陆俭国. 应用“模糊概率加权移动预测”研究电接触性能[J]. 电工技术学报, 2006, 21(3): 83-88.
Li Zhigang, Yao Fang, Lu Jianguo. Research on electrical contact performance using fuzzy probability weight prediction model[J]. Transactions of China Electrotechnical Society, 2006, 21(3): 83-88.
[11] 李志刚, 刘伯颖, 李玲玲, 等. 基于小波包变换及RBF神经网络的继电器寿命预测[J]. 电工技术学报, 2015, 30(14): 233-240.
Li Zhigang, Liu Boying, Li Lingling, et al. Life prediction of relay based on wavelet packet transform and RBF neural network[J]. Transactions of China Electrotechnical Society, 2015, 30(14): 233-240.
[12] Zhang Xin, Huang Ronghui, Yao Senjing, et al. Mechanical life prognosis of high voltage circuit breakers based on support vector machine[C]//2015 11th International Conference on Natural Computation, Zhangjiajie, 2015.
[13] 肖迁, 李文华, 李志刚, 等. 基于改进的小波-BP神经网络的风速和风电功率预测[J]. 电力系统保护与控制, 2014, 42(15): 80-86.
Xiao Qian, Li Wenhua, Li Zhigang, et al. Wind speed and power prediction based on improved wavelet-BP neural network[J]. Power System Protection and Control, 2014, 42(15): 80-86.
[14] Gebraeel N Z, Lawley M A. A neural network degradation model for computing and updating residual life distributions[J]. IEEE Transactions on Automation Science and Engineering, 2008, 5(1): 154-163.
[15] 袁娜, 杨鹏, 刘作军. 利用平均影响值和概率神经网络的步态识别[J]. 哈尔滨工程大学学报, 2015, 36(2): 181-185.
Yuan Na, Yang Peng, Liu Zuojun. Gait recognition by the mean impact value and probability neural network[J]. Journal of Harbin Engineering University, 2015, 36(2): 181-185.
[16] 张顶学. 遗传算法与粒子群算法的改进及应用[D]. 武汉: 华中科技大学, 2007.
[17] 姚仲敏, 潘飞, 沈玉会, 等. 基于GA-BP和POS-BP神经网络的光伏电站出力短期预测[J]. 电力系统保护与控制, 2015, 43(20): 83-89.
Yao Zhongmin, Pan Fei, Shen Yuhui, et al. Short-term prediction of photovoltaic power generation output based on GA-BP and POS-BP neural network[J]. Power System Protection and Control, 2015, 43(20): 83-89.
[18] Hecht-Nielsen R. Theory of the backpropagation neural network[C]//International Joint Conference on Neural Networks, 1989: 593-605.
[19] Guo Pengfei, Wang Xuezhi, Han Yingshi. The enhanced genetic algorithms for the optimization design[C]//3rd International Conference on Biomedical Engineering and Informatics, Yantai, 2010: 2990-2994.
[20] 罗建春, 晁勤, 罗洪, 等. 基于LVQ-GA-BP神经网络光伏电站出力短期预测[J].电力系统保护与控制, 2014, 42(13): 89-94.
Luo Jianchun, Chao Qin, Luo Hong, et al. PV short-term output forecasting based on LVQ-GA-BP neural network[J]. Power System Protection and Control, 2014, 42(13): 89-94.
[21] 史丽萍, 汤家升, 王攀攀, 等. 采用最优小波树和改进BP神经网络的感应电动机定子故障诊断[J]. 电工技术学报, 2015, 30(24): 38-45.
Shi Liping, Tang Jiasheng, Wang Panpan, et al. Stator fault diagnosis of induction motors using the optimal wavelet tree and improved BP neural network[J]. Transactions of China Electrotechnical Society, 2015, 30(24): 38-45.