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

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基于ReliefF-mRMR與IAO-SVM的變壓器故障診斷

來源:電工電氣發(fā)布時間:2023-02-07 10:07 瀏覽次數:396

基于ReliefF-mRMR與IAO-SVM的變壓器故障診斷

張佳豪1,楊國華1,2,趙藝青1,張兆坤1,李志遠1
(1 寧夏大學 物理與電子電氣工程學院,寧夏 銀川 750021;
2 寧夏電力能源安全重點實驗室,寧夏 銀川 750004)
 
    摘 要:為進一步提高變壓器故障診斷準確率,提出一種基于 ReliefF-mRMR 與 IAO-SVM 結合的變壓器故障診斷模型。采用 ReliefF 和最大相關最小冗余 (mRMR) 算法對變壓器故障數據進行特征優(yōu)選;引入混沌反向學習和自適應混合變異策略改進天鷹優(yōu)化算法,并對最優(yōu)特征集合和支持向量機 (SVM) 參數聯合尋優(yōu),構建最佳故障診斷模型;利用已有變壓器故障數據對所提模型仿真實驗,并與常用故障診斷模型灰狼算法支持向量機 (GWO-SVM)、天鷹優(yōu)化算法支持向量機 (AO-SVM) 相比較, 準確率分別提高了 10.76% 和 6.15%,高達 95.38%,結果表明所提模型能有效提高變壓器故障診斷精度。
    關鍵詞: 變壓器;故障診斷;特征優(yōu)選;改進天鷹優(yōu)化算法;支持向量機
    中圖分類號:TM407     文獻標識碼:A     文章編號:1007-3175(2023)01-0001-07
 
Fault Diagnosis Method of Transformer Based on ReliefF-mRMR and IAO-SVM
 
ZHANG Jia-hao1, YANG Guo-hua1,2, ZHAO Yi-qing1, ZHANG Zhao-kun1, LI Zhi-yuan1
(1 College of Physics and Electrical and Electronic Engineering, Ningxia University, Yinchuan 750021, China;
2 Ningxia Key Laboratory of Power and Energy Security, Yinchuan 750004, China)
 
    Abstract: The paper is aimed at putting forward a transformer fault diagnosis model based on ReliefF-mRMR and IAO-SVM to further increase its accuracy. In order to build an optimal fault diagnosis model, the authors use ReliefF and mRMR for feature optimization of transformer fault data, combine chaotic backward learning and adaptive mixed mutation strategy to improve aquila optimizer, and make joint optimization of optimal feature set and support vector machine (SVM) parameters. By doing simulation experiment of the existing transformer fault data and comparing the optimal fault diagnosis model with gray wolf algorithm support vector machine (GWO-SVM) and aquila optimizer support vector machine (AO-SVM), it is found that the accuracy of the optimal fault diagnosis model rise to 95.38% with the growth rate of 10.76% and 6.15% respectively, verifying its high accuracy of transformer fault diagnosis.
    Key words: transformer; fault diagnosis; feature optimization; improved aquila optimizer; support vector machine
 
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