基于多特征提取和麻雀搜索算法优化XGBoost的变压器绕组松动诊断方法  

Transformer winding looseness diagnosis method based on multiplefeature extraction and sparrow search algorithm optimized XGBoost

作　　者：马宏忠[1] 肖雨松 颜锦 孙永腾 MA Hongzhong;XIAO Yusong;YAN Jin;SUN Yongteng(College of Energy and Electrical Engineering,Hohai University,Nanjing 211100,China;Hengyang Power Supply Branch,State Grid Hunan Electric Power Co.,Hengyang 421200,China)

机构地区：[1]河海大学能源与电气学院,江苏南京211100 [2]国网湖南省电力有限公司衡阳供电分公司,湖南衡阳421200

出　　处：《电机与控制学报》2024年第6期87-97,共11页Electric Machines and Control

基　　金：国家自然科学基金(51577050);国家电网江苏省电力有限公司重点科技项目(J2022047)。

摘　　要：针对使用单一特征量诊断变压器绕组松动,在不同负载条件下存在交叠和抗干扰能力不足的问题,提出一种基于核主成分分析(KPCA)和改进麻雀搜索算法(SSA)优化极端梯度提升(XGBoost)的变压器绕组松动振动诊断方法。首先,从时域、频域和熵值3个维度提取适用于变压器多传感器振动信号的多种特征量;其次,通过网格搜索优化的KPCA对特征量进行降维;最后,构建基于XGBoost的故障诊断模型,并采用改进麻雀搜索算法调参,实现不同电流大小下变压器绕组松动故障准确识别。以某110 kV变压器为对象进行实验验证,诊断结果表明,所提取的特征量能够准确反映故障特征,抗干扰能力更强,诊断模型故障诊断准确率为99.00%,相比于其他诊断算法准确率和稳定性更高,在不同负载情况下均有良好的识别效果。In order to solve the problem of overlap and insufficient anti-interference ability under different load conditions in diagnosing transformer winding looseness using a single feature quantity,a vibration signal diagnosis method for transformer winding looseness based on kernel principal component analysis(KPCA)and extreme gradient boosting(XGBoost)optimized by improved sparrow search algorithm(SSA)was proposed.Firstly,feature quantities in vibration signals were extracted from three dimensions:time domain,frequency domain,and entropy;Then,the feature quantity was dimensionally reduced through grid search optimized KPCA;Finally,a fault diagnosis model based on XGBoost was constructed and sparrow search algorithm was used to optimize the parameters for achieving accurate identification of transformer winding looseness faults under different currents.The experimental verification was conducted on a 110 kV transformer.The diagnosis results show that the extracted feature quantities can accurately reflect the fault characteristics,have stronger anti-interference ability,and the diagnostic accuracy rate of the diagnostic model is 99.00%.Compared with other diagnostic algorithms,the accuracy and stability are higher,and have good recognition effects under different load conditions.

关 键 词：变压器振动 绕组松动 核主成分分析 极端梯度提升 麻雀搜索算法 故障诊断 

分 类 号：TM41[电气工程—电器]