摘要
针对SVM在变压器故障诊断中存在诊断精度不高和BWO算法存在易陷入局部最优的问题,提出一种基于EBWO-SVM的变压器故障诊断方法。首先通过引入准反向学习策略和旋风式觅食策略对BWO算法进行改进,然后将EBWO算法与粒子群优化算法、灰狼优化算法、鲸鱼优化算法、白鲸优化算法在6种测试函数上进行寻优测试,验证了EBWO算法的优越性。其次利用EBWO算法对SVM中的核函数参数g和C进行优化,从而提高SVM的分类能力。最后提出其他方法与EBWO-SVM模型进行对比。结果表示:所构建的EBWO-SVM变压器故障诊断模型与BWO-SVM、WOA-SVM、GWO-SVM、PSO-SVM相比,综合诊断精度分别提高了7.7%、9.7%、11.6%、15.4%,且稳定性更强,验证了EBWO-SVM模型的可行性与有效性。
Aiming at the problems of low diagnostic accuracy of SVM and easy to fall into local optimum of BWO algorithm in transformer fault diagnosis,a transformer fault diagnosis method based on EBWO-SVM is proposed.Firstly,the BWO algorithm is improved by introducing quasi-opposition-based learning strategy and cyclone foraging strategy,and then the EBWO algorithm and particle swarm optimization algorithm,grey wolf optimization algorithm,whale optimization algorithm,and beluga optimization algorithm are tested for optimality seeking on six test functions,which verifies the superiority of the EBWO algorithm.Secondly,the EBWO algorithm is used to optimise the kernel function parameters g and C in SVM so as to improve the classification ability of SVM.Finally other methods are proposed to compare with the EBWO-SVM model.The results indicate that the constructed EBWO-SVM transformer fault diagnosis model improves the comprehensive diagnostic accuracy by 7.7%,9.7%,11.6%,and 15.4%compared with BWO-SVM,WOA-SVM,GWO-SVM,and PSO-SVM,respectively,and is more stable,which verifies the feasibility and effectiveness of the EBWO-SVM model.
作者
汪繁荣
李州
Wang Fanrong;Li Zhou(School of Electrical and Electronic Engineering,Hubei University of Technology,Wuhan 430074,China)
出处
《电子测量技术》
北大核心
2024年第10期101-107,共7页
Electronic Measurement Technology
基金
国家自然科学基金(61903129)项目资助。
关键词
支持向量机
白鲸优化算法
变压器
故障诊断
准反向学习策略
旋风式觅食策略
support vector machines
beluga whale optimization
transformers
fault diagnosis
quasi-opposition-based learning strategy
cyclonic foraging strategy
