基于改进SSA优化MDS-SVM的变压器故障诊断方法  

Transformer fault diagnosis method based on improved SSA optimized MDS-SVM

作　　者：谢国民[1] 蔺晓雨 XIE Guo-min;LIN Xiao-yu(Faculty of Electrical and Control Engineering,Liaoning Technical University,Huludao 125105,China)

机构地区：[1]辽宁工程技术大学电气与控制工程学院,辽宁葫芦岛125105

出　　处：《控制与决策》2023年第2期459-467,共9页Control and Decision

基　　金：国家自然科学基金项目(51974151);辽宁省教育厅重点实验室基金项目(LJZS003)。

摘　　要：为了提高变压器故障诊断精度,提出一种基于改进SSA优化MDS-SVM的变压器故障诊断方法.首先,利用多维尺度缩放法(multiple dimensional scaling,MDS)对20维变压器故障特征数据进行特征提取,降低高维数据存在的稀疏性和多重共线性;其次,引入樽海鞘群算法(salp swarm algorithm,SSA),并对该算法进行改进,增置信赖机制和突变,以提高算法的收敛速度和收敛能力;然后,通过与原始SSA、PSO、GWO和β-GWO算法进行寻优测试对比来验证改进SSA算法的优越性;最后,使用改进SSA算法对MDS降低维数和支持向量机(support vector machine,SVM)的参数联合寻优,构建新的故障诊断模型.分析并比较其与常用算法优化的SVM故障诊断模型、BP神经网络(back propagation neural network,BPNN)、K最近邻(K-nearest neighbor,KNN)以及随机森林(random forest,RF)故障诊断模型的故障诊断精确度,结果表明,基于改进SSA的MDS-SVM变压器故障诊断模型的精确度高于其他算法模型,且泛化能力较强.In order to improve the accuracy of transformer fault diagnosis,a transformer fault diagnosis method based on an improved SSA optimized MDS-SVM is proposed.Firstly,a multi-dimensional scaling(MDS)method is used to extract features from 20 dimensional transformer fault feature data to reduce the sparsity and multicollinearity of highdimensional data.Then,the paper introduces a salp swarm algorithm(SSA)and improves the algorithm by adding trust mechanism and mutation to improve the convergence speed and ability of the algorithm.By comparing with the original SSA,PSO,GWO,andβ-GWO,the improved SSA algorithm is tested to verify its superiority.Finally,the improved algorithm is used to reduce the dimension of the MDS and optimize the parameters of a support vector machine(SVM)to build a new fault diagnosis model.The fault diagnosis accuracy is analyzed and compared with that of the SVM fault diagnosis model optimized by common algorithms,the BP neural network(BPNN),the K-nearest neighbor(KNN)and random forest(RF)fault diagnosis models.The results show that the accuracy of the MDS-SVM transformer fault diagnosis model based on the improved SSA is higher than that of other algorithm models,and the generalization ability is stronger.

关 键 词：变压器 故障诊断 多维尺度缩放法 樽海鞘算法 支持向量机 算法改进 

分 类 号：TM407[电气工程—电器] TP18[自动化与计算机技术—控制理论与控制工程]