基于多输出衰减径向基函数神经网络的电网故障诊断

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（Mu

Fault diagnosis of power grids based on multi-output decay radial basis function neural network

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.