本文關(guān)鍵詞：基于徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的電網(wǎng)模糊元胞故障診斷，，由筆耕文化傳播整理發(fā)布。

摘要：

提出了基于徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的電網(wǎng)模糊元胞故障診斷方法，旨在有效解決神經(jīng)網(wǎng)絡(luò)應(yīng)用于電網(wǎng)故障診斷所面臨的適應(yīng)網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)變化的可移植性問題。該方法以單個線路、母線和變壓器為元胞對象，以保護(hù)各元胞的所有關(guān)聯(lián)保護(hù)和對應(yīng)的斷路器為輸入，建立了元胞通用神經(jīng)網(wǎng)絡(luò)診斷模型，并給出了故障診斷時模型的自動生成方法。此外，考慮到電網(wǎng)故障信息存在不完備性和不確定性，本文采用模糊矢狀圖來描述電網(wǎng)元件、保護(hù)和斷路器之間的邏輯推理關(guān)系，并提取出蘊(yùn)含不確定性的模糊推理規(guī)則，用于訓(xùn)練元胞通用神經(jīng)網(wǎng)絡(luò)。算例仿真結(jié)果表明，該方法簡單、有效，能處理各種復(fù)雜故障情況，且能有效適應(yīng)網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)的變化，具有良好的容錯性和可移植性。

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.

  本文關(guān)鍵詞：基于徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的電網(wǎng)模糊元胞故障診斷，由筆耕文化傳播整理發(fā)布。