多策略改进MPA算法与HKELM的变压器故障辨识  

Transformer fault identification based on multi-strategy improved MPA algorithm and HKELM

作　　者：谢国民[1] 刘东阳 刘明[2] Xie Guomin;Liu Dongyang;Liu Ming(Faculty of Electrical and Control Engineering,Liaoning Technical University,Huludao 125105,China;Department of Mathematics and Computer Science,Chaoyang Teachers College,Chaoyang 122000,China)

机构地区：[1]辽宁工程技术大学电气与控制工程学院,葫芦岛125105 [2]朝阳师范高等专科学校数学计算机系,朝阳122000

出　　处：《电子测量与仪器学报》2023年第4期172-182,共11页Journal of Electronic Measurement and Instrumentation

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

摘　　要：为解决目前变压器故障诊断精度低的问题,提出一种多策略改进海洋捕食者算法(MPA)与混合核极限学习机(HKELM)的变压器故障辨识方法。首先通过核主成分分析法(KPCA)对高维线性不可分的变压器故障数据进行降维,获取特征支持数据;然后通过伯努利混沌映射、改进阶段转换判据、最佳候选者等策略综合改进MPA,加强全局开发能力;最后使用改进的IMPA算法对HKELM的参数寻优,构建变压器故障诊断模型。为验证模型有效性,分析比较常用算法优化的HKELM的4种变压器故障诊断模型。其中IMPA-HKELM的诊断精度为94.7%,相比于另外3种基础算法优化的模型,诊断精度分别提升了5.4%、8%、10.7%。结果表明,提出模型有效提升了故障诊断的分类性能,并实现了较高的故障诊断精度。For the purpose of tackling the difficulties of the low accuracy of transformer fault diagnosis,a transformer fault identification method based on multi-strategy improved ocean predator algorithm(MPA)and hybrid kernel extreme learning machine(HKELM)has been proposed.Firstly,kernel principal component analysis(KPCA)is applicable to decrease the dimension of high-dimensional linear inseparable transformer fault data and it is also used to obtain feature support data.Then,the MPA is comprehensively improved through strategies such as Bernoulli chaotic mapping,improved stage transition criterion,and best candidate to strengthen the global development ability.Finally,the improved IMPA algorithm is used to optimize the parameters of HKELM and construct the transformer fault diagnosis model.Aiming to validate the validity of the model,four transformer fault diagnosis models of HKELM optimized by common algorithms are analyzed and compared.The diagnostic accuracy of IMPA-HKELM is 94.7%,compared with the other three basic algorithms,the diagnostic accuracy is improved by 5.4%,8%and 10.7%respectively.The results show that the proposed model effectively improves the classification performance of fault diagnosis and achieves higher fault diagnosis accuracy.

关 键 词：故障诊断 油浸式变压器 伯努利混沌映射 混合核极限学习机 核主成分分析 

分 类 号：TN06[电子电信—物理电子学]