Suzhou Electric Appliance Research Institute
期刊號: CN32-1800/TM| ISSN1007-3175

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基于NRS的GWO-SVM變壓器故障診斷方法研究

來源:電工電氣發(fā)布時間:2022-02-28 11:28 瀏覽次數(shù):620

基于NRS的GWO-SVM變壓器故障診斷方法研究

徐偉進(jìn)1,徐煒彬1,張煒華1,李想1,吳振2
(1 國網(wǎng)吉林省電力有限公司長春供電公司,吉林 長春 130000;
2 長春工業(yè)大學(xué) 電氣與電子工程學(xué)院,吉林 長春 130012)
 
    摘 要:針對油中溶解氣體分析法 (DGA) 不能有效反映變壓器的不同故障且診斷準(zhǔn)確率低的問題,通過鄰域粗糙集 (NRS) 對變壓器故障數(shù)據(jù)比值進(jìn)行約簡,得出一組新比值作為診斷樣本,進(jìn)而利用灰狼算法 (GWO) 與支持向量機(jī) (SVM) 結(jié)合的模型進(jìn)行故障診斷。實(shí)驗(yàn)分析表明,利用 NRS 對變壓器故障數(shù)據(jù)約簡能夠有效提高變壓器故障準(zhǔn)確率,同時驗(yàn)證了 GWO-SVM 模型對于變壓器故障診斷的良好適用性。
    關(guān)鍵詞:變壓器;故障診斷;鄰域粗糙集;支持向量機(jī);灰狼算法
    中圖分類號:TM407     文獻(xiàn)標(biāo)識碼:A     文章編號:1007-3175(2022)02-0009-05
 
Research on GWO-SVM Transformer Fault
Diagnosis Method Based on NRS
 
XU Wei-jin1, XU Wei-bin1, ZHANG Wei-hua1, LI Xiang1, WU Zhen2
(1 Changchun Power Supply Company, State Grid Jilin Electric Power Co., Ltd, Changchun 130000, China;
2 School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China)
 
    Abstract: Dissolved gas analysis (DGA) in oil cannot reflect the different faults of the transformer effectively, and the diagnosis accuracy is low. This paper simplified the ratio of transformer fault data by using neighborhood rough set (NRS) to solve this problem. It derived a new set of ratios as a diagnostic sample from the simplified data.Furthermore, it used the gray wolf optimize (GWO) combined with the support vector machine (SVM) model for fault diagnosis.The experiment analysis shows that the use of NRS to simplify transformer fault data could effectively improve the accuracy of transformer faults. At the same time, it verifies the applicability of the GWO-SVM model for transformer fault diagnosis.
    Key words: transformer; fault diagnosis; neighborhood rough set; support vector machine; gray wolf optimize
 
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