摘要
为提高电网故障诊断神经网络模型的构建速度,提出了一种基于多输出衰减径向基函数(Multi-output Decay Radial Basis Function,MDRBF)神经网络的故障诊断方法。DRBF神经网络不需训练即能以任意精度一致逼近任意连续多变量函数。介绍了单输出DRBF(Single-output DRBF,SDRBF)神经网络,分析了其存在的不足,即只能处理单输出变量问题,不能直接应用于电网故障诊断。在此基础上,根据电网元件的故障特点,提出了将SDRBF神经网络演变为多输出DRBF(Multi-Output DRBF,MDRBF)神经网络的拓展策略,以满足电网故障诊断的多输出变量需求。以4母线输电网络作为仿真系统,算例结果表明,该方法具有实现简单、容错性好、鲁棒性强等特点。
To improve the rate of construction of neural network based model of power grids fault diagnosis, a method based on multi-output decay radial basis function （MDRBF） neural network for fault diagnosis is proposed. DBRF neural network can uniformly approximate any continuous multivariate functions with arbitrary precision without training. The single-output DRBF （SDRBF） neural network is introduced, and its shortage in fault diagnosis of power grids is analyzed. Since single-output DRBF （SDRBF） neural network can only solve the single-variable output problem, therefore, it can not be used directly in fault diagnosis of power grids. On this basis, a strategy for expanding the SDRBF neural network to the MDRBF neural network is proposed according to the fault characteristic of electric components. MDRBF neural network is able to satisfy the multi-variable output need of fault diagnosis of power grids. A four-bus transmission grid is adopted as a simulation system and the results show that the proposed diagnostic method based on MDRBF neural network is simple and has good fault-tolerance and strong robustness. This work is sunoorted bv National Natural Science Foundatinn of Ohina （Nln ~ 11 （~70~,~
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2013年第21期38-45,共8页
Power System Protection and Control
基金
国家自然科学基金项目(51107048)~~
关键词
电力系统
故障诊断
衰减径向基函数神经网络
容错性
鲁棒性
power system
fault diagnosis
decay radial basis function neural network
fault-tolerance
robustness
