基于BP神经网络的高压断路器故障诊断
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摘要
为诊断与分析高压断路器故障,本文提出了基于BP神经网络的高压断路器故障诊断方法。该方法利用高压断路器典型分合闸线圈电流-时间曲线,能反映其机械故障状况的特点,将仿真输出数据与故障编码比较获得诊断结果。该方法只需一组完整的故障数据作为网络的训练和测试输入,就能够诊断出高压断路器操动机构是否出现异常情况,以及确定出现故障的类型。本文以MATLAB2014b为试验平台,用实际数据作为训练样本和测试样本进行仿真分析,其输出结果与期望输出一致,验证了该方法是一种有效的高压断路器故障诊断方法,具有广阔的应用前景。
A novel BP neural network model for fault diagnosis of high voltage vacuum circuit breaker(VCB) was developed.The influence of a variety of possible mechanical faults on the characteristics of the time evolution of a typical switching coil current,divided into five distinctive stages,was mathematically modeled,experimentally evaluated and numerically simulated with MATLAB2014 b as the platform to diagnose a specific failure of VCB.A complete set of the measured abnormal switching-coil current evolution were acquired as the training,testing and simulation samples.The preliminary results show that a specific VCB fault has a major impact on the time-dependent switching coil current.The predicted,simulated and measured results were found to be in good agreement.We suggest that the newly-developed BP neural network model be capable of effectively diagnosing fault of high voltage VCB.
A novel BP neural network model for fault diagnosis of high voltage vacuum circuit breaker(VCB) was developed.The influence of a variety of possible mechanical faults on the characteristics of the time evolution of a typical switching coil current,divided into five distinctive stages,was mathematically modeled,experimentally evaluated and numerically simulated with MATLAB2014 b as the platform to diagnose a specific failure of VCB.A complete set of the measured abnormal switching-coil current evolution were acquired as the training,testing and simulation samples.The preliminary results show that a specific VCB fault has a major impact on the time-dependent switching coil current.The predicted,simulated and measured results were found to be in good agreement.We suggest that the newly-developed BP neural network model be capable of effectively diagnosing fault of high voltage VCB.
引文
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[2] Hydraulic Operating Mechanisms for High Voltage Circuit Breakers:Progress Evolution and Future Trends[J].Science China Technological Sciences,2011,(01):116-125
[3] YANG Zhuangzhuang,LIU Yang,ZHOU Guoming,et al.Method of Mechanical Fault Intelligent Diagnosis Based on Vibration Signal of High Voltage Circuit Breaker[J].HIGH VOLTAGE APPARATUS,2014,(04):1-6
[4] 黄建,胡晓光,巩玉楠,等.高压断路器机械故障诊断专家系统设计[J].电机与控制报,2011,15(10):43-49
[5] 黄凌洁,王玮,吴振升,等.基于模糊理论的高压断路器故障诊断模型的研究[J].高压电器,2008,44(3):246-249
[6] 钟建英,刘洋,林莘,等.基于振动信号特征的高压断路器机械故障诊断技术研究[J].高压电,2013,49(9):49-54
[7] Li Zhang,Jia-Qiang Zhao,Xu-Nan Zhang,et al.Study of a New Improved PSO-BP Neural Network Algorithm[J].Journal of Harbin Institute of Technology,2013,(05):106-112
[8] 谢文靖.高压断路器智能故障诊断方法研究[D].昆明:云南大学,2013
[9] 徐国政,张节容,钱家骊,等.高压断路器原理和应用[ M].北京:清华大学出版社,2000
[10] 黄凌洁.高压断路器状态监测与故障诊断方法的研究[D].北京:北京交通大学,2007
[11] 岳小斌,练刚.基于粒子群优化神经网络的高压断路器故障诊断[J].电力学报,2011,(01):41-44+49