RBF神经网络在断路器故障诊断中的应用  

Application of RBF neural network in fault diagnosis of circuit breaker

作　　者：郭凤仪[1] 马文龙[1] 李斌[1] 

机构地区：[1]辽宁工程技术大学电气与控制工程学院,辽宁葫芦岛125105

出　　处：《传感器与微系统》2010年第9期128-130,共3页Transducer and Microsystem Technologies

基　　金：辽宁省优秀人才支持计划资助项目(2007R23)

摘　　要：针对RBF神经网络可在系统参数未知情况下自动建立动态模型,快速跟踪非线性函数,且有很好分类能力等特点,在分析高压断路器分闸线圈时间信号的基础上,提出了RBF神经网络对高压断路器进行故障分类的基本方法。与BP神经网络的诊断结果进行对比,RBF神经网络具有收敛速度快,输出误差和离散性小的优点,并借助仿真结果论证了该实验方法对高压断路器故障诊断的有效性。Aiming at the characteristics of RBF neural network which can automatically create a systems dynamic model under the circumstances of unknown parameters, fast track non-linear function and classification capacity. On the basis of the analysis of time signal of breaking coil of high-voltage circuit breaker, a basic method which use RBF neural network to classify faults for high-vohage circuit breaker has been proposed. Compared with the diagnosis result of BP neural network,it shows that RBF neural network has capability of fast convergence, small output error and discrete. By means of the simulation results the effectiveness of adopting this method to diagnose fault for high-voltage circuit breaker has been proved.

关 键 词：RBF神经网络 故障诊断 高压断路器 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]