本文關鍵詞：基于徑向基函數(shù)神經(jīng)網(wǎng)絡的電網(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)絡應用于電網(wǎng)故障診斷所面臨的適應網(wǎng)絡拓撲結構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，以保護各元胞的所有關聯(lián)保護和對應的斷路器為輸入，建立了元胞通用神經(jīng)網(wǎng)絡診斷模型，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、保護和斷路器之間的邏輯推理關系，并提取出蘊含不確定性的模糊推理規(guī)則，用于訓練元胞通用神經(jīng)網(wǎng)絡。算例仿真結果表明，該方法簡單、有效，能處理各種復雜故障情況，且能有效適應網(wǎng)絡拓撲結構的變化，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.

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