基于模糊C均值聚类和DBO-LSSVM的变压器故障诊断方法研究  

Research on Transformer Fault Diagnosis Method Based on Fuzzy C-mean Clustering and DBO-LSSVM

作　　者：王琼宇 冯馨瑜 WANG Qiongyu;FENG Xinyu(School of Electronic Information,Xi'an Polytechnic University,Xi'an 710048,China;State Grid Weinan Power Supply Company,Weinan 714000,China)

机构地区：[1]西安工程大学电子信息学院,陕西西安710048 [2]国家电网渭南供电公司,陕西渭南714000

出　　处：《电工技术》2025年第5期69-71,78,共4页Electric Engineering

摘　　要：针对单一SVM固有二分类性能差及多个分类器使用同一个参数分类精度低等问题,提出一种基于模糊C均值聚类和DBO-LSSVM的变压器故障诊断方法。首先,利用模糊C均值方法将样本聚类,构造一个完全二叉树结构,每个叶子节点采用LSSVM分类器;其次,利用蜣螂优化算法(DBO)优化各个LSSVM分类器的核参数σ和惩罚系数C;最后,采用最优参数在完全二叉树自上而下逐层进行故障诊断,并与不同算法对比。仿真结果表明,所提方法在变压器故障诊断方面具有较高的诊断精度。Aiming at the problems of poor binary classification performance inherent in a single SVM and low classification accuracy of multiple classifiers using the same parameter,a transformer fault diagnosis method based on fuzzy C-mean clustering and DBO-LSSVM is proposed.Firstly,the fuzzy C-mean clustering method is used to cluster the samples,and a complete binary tree structure is constructed,with each leaf node adopting an LSSVM classifier;secondly,the dung beetle optimization algorithm(DBO)is used to optimize the kernel parameter and the penalty coefficient of each LSSVM classifier;finally,the optimal parameter is used to carry out the fault diagnosis in the complete binary tree layer by layer from the top to the bottom and analyzed and compared with the different algorithms.Simulation results show that the proposed method has high diagnostic accuracy in transformer fault diagnosis.

关 键 词：变压器 故障诊断 模糊C均值聚类 DBO LSSVM 

分 类 号：TM615[电气工程—电力系统及自动化]