基于神經(jīng)網(wǎng)絡綜合分析的變壓器油色譜在線監(jiān)測系統(tǒng)
史晏廷1，楊波1，陳爾奎1，李錦川2，譚小艷1
1 山東科技大學 信息與電氣工程學院，山東 青島 266590；
2 東北電力大學 電氣工程學院，吉林 吉林 132012
摘 要：油色譜在線監(jiān)測是電力變壓器在線監(jiān)測領域常用的方法之一。變壓器故障診斷的結果將直接作為變壓器是否需要檢修的決策依據(jù)，鑒于變壓器故障原因的復雜性，僅靠單一的故障診斷方法很難滿足故障診斷的要求，故將傳統(tǒng)故障診斷方法與BP神經(jīng)網(wǎng)絡方法通過Borda模型相結合，以提高變壓器故障診斷的準確率，最后用C#語言設計開發(fā)了故障診斷系統(tǒng)。該系統(tǒng)改變了以往的定期試驗模式，實現(xiàn)了變壓器狀態(tài)在線監(jiān)測和分析。
關鍵詞：變壓器在線監(jiān)測；故障診斷；BP神經(jīng)網(wǎng)絡；Borda模型；C#語言
中圖分類號：TM411 文獻標識碼：A 文章編號：1007-3175(2013)06-0045-05
Online Monitoring System of Transformer Oil Chromatography Based on Neural Network Integrated Analysis
SHI Yan-ting1, YANG Bo1, CHEN Er-kui1, LI Jin-chuan2, TAN Xiao-yan1
1 College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, China;
2 Electrical Engineering College, Northeast Dianli University, Jilin 132012, China
Abstract: Oil chromatography monitoring is one of the commonly used methods in the online monitoring field of power transformers. The results of fault diagnosis for a transformer will provide a direct basis for determining whether the transformer needs an overhaul. Due to the complex reasons of transformer faults, it is difficult for a single diagnosis method to meet the requirements of fault diagnosis. In order to improve the accuracy of fault diagnosis, this paper combined the traditional fault diagnosis method with the back propagation (BP) neural network method by the Borda model. The fault diagnosis system developed by C# language improved the traditional mode of routine test and achieved online monitoring and analysis for the state of power transformers.
Key words: transformer online monitoring; fault diagnosis; back propagation neural network; Borda model; C# language
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