Suzhou Electric Appliance Research Institute
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基于分步特征選取和WOA-LSSVM的變壓器故障診斷

來源：電工電氣發(fā)布時(shí)間：2024-08-30 13:30瀏覽次數(shù)：32

基于分步特征選取和WOA-LSSVM的變壓器故障診斷

謝樂，楊浙，潘成南
(國(guó)網(wǎng)浙江省電力有限公司慈溪市供電公司，浙江 慈溪 315300)
 
    摘 要：為了提高變壓器故障診斷的精度，保障電網(wǎng)的穩(wěn)定運(yùn)行，提出了一種基于 ReliefF 算法與界標(biāo)等距映射(L-Isomap)的分步特征選取和鯨魚群算法(WOA)優(yōu)化最小二乘支持向量機(jī)(LSSVM)的故障診斷模型。選取 7 種常見故障特征油中溶解氣體分析(DGA)氣體以及其構(gòu)造出的16 組比值作為初始特征集，利用 ReliefF 算法分別對(duì)初始特征集進(jìn)行特征選擇，再利用 L-Isomap 算法對(duì)融合后的特征集進(jìn)行降維處理，將降維處理后的特征集作為故障特征向量代入診斷模型，故障診斷模型采用 WOA-LSSVM 進(jìn)行訓(xùn)練與測(cè)試。實(shí)驗(yàn)結(jié)果表明，診斷模型的精度高達(dá)98.31%，相比于其他模型擁有更高的診斷精度。
    關(guān)鍵詞： 變壓器；故障診斷；分步特征選?。唤稻S；鯨魚群算法；最小二乘支持向量機(jī)
    中圖分類號(hào)：TM406 ；TM411     文獻(xiàn)標(biāo)識(shí)碼：B     文章編號(hào)：1007-3175(2024)08-0031-06
 
Transformer Fault Diagnosis Based on Stepwise Feature
Selection and WOA-LSSVM
 
XIE Le, YANG Zhe, PAN Cheng-nan
（Cixi Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd,Cixi 315300, China）
 
    Abstract: In order to improve the accuracy of transformer fault diagnosis and ensure the stable operation of power system. In this paper,proposing a stepwise feature selection based on ReliefF algorithm and landmark isomap (L-Isomap) and a fault diagnosis model for whale optimization algorithm (WOA) least squares support vector machine (LSSVM). The method first selected 7 common fault characteristics dissolved gas analysis in oil (DGA) gas and constructed 16 sets of ratios as the initial feature set. Secondly, the ReliefF algorithm was used to perform feature selection on the initial feature set respectively, and then the L-Isomap algorithm was used to reduce the dimensionality of the fused feature set, and the dimensionality reduction feature set was substituted into the diagnostic model as a fault feature vector, and the fault diagnosis model was trained and tested by WOA-LSSVM. The experimental results show that the accuracy of the diagnostic model is as high as 98.31%, which is higher diagnostic accuracy than that of other models.
    Key words: transformer; fault diagnosis; stepwise feature selection; dimensionality reduction; whale optimization algorithm; least squares support vector machine
 
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