Fuzzy Cellular Fault Diagnosis of Power Grids Based on Radia

  本文关键词：基于径向基函数神经网络的电网模糊元胞故障诊断，，由笔耕文化传播整理发布。

摘要：

提出了基于径向基函数神经网络的电网模糊元胞故障诊断方法，旨在有效解决神经网络应用于电网故障诊断所面临的适应网络拓扑结构变化的可移植性问题。该方法以单个线路、母线和变压器为元胞对象，以保护各元胞的所有关联保护和对应的断路器为输入，建立了元胞通用神经网络诊断模型，并给出了故障诊断时模型的自动生成方法。此外，考虑到电网故障信息存在不完备性和不确定性，本文采用模糊矢状图来描述电网元件、保护和断路器之间的逻辑推理关系，并提取出蕴含不确定性的模糊推理规则，用于训练元胞通用神经网络。算例仿真结果表明，该方法简单、有效，能处理各种复杂故障情况，且能有效适应网络拓扑结构的变化，具有良好的容错性和可移植性。

Abstract： 

A fuzzy cellular fault diagnosis method of power grids based on the radial basis function neural network is proposed for solving the transportability problem of adapting to the network topology changes when applying neural networks to fault diagnosis of power grids.This method takes single line,bus and transformer as a cellular obj ect,and takes all the associated protective relays (PRs) and circuit breakers (CBs) used to protect the cellule as inputs to develop a generalized cellular neural network diagnostic model.Moreover,a method for automatic generation of the diagnostic model during fault diagnosis is presented.In addition,taking into account the fault information”s characteristic of incompleteness and uncertainty,a fuzzy sagittal diagram for each cellular type is adopted to describe the logic reasoning relationships between the components,PRs, and CBs.From the diagram,multiple fuzzy reasoning rules containing uncertainties can be extracted to train the generalized cellular neural network.The simulation results show that the proposed method is simple,efficient,and can solve different complex faults. Moreover, it can effectively adapt to network topology changes and has good fault tolerance and transportability.

  本文关键词：基于径向基函数神经网络的电网模糊元胞故障诊断，由笔耕文化传播整理发布。