基于数据均衡化与改进鲸鱼算法优化核极限学习机的变压器故障诊断方法  

Transformer Fault Diagnosis Method Based on Data Equalization and Kernel-based Extreme Learning Machine of Improved Whale Algorithm

作　　者：王雨虹[1] 孙远星 包伟川 陈子春 WANG Yuhong;SUN Yuanxing;BAO Weichuan;CHEN Zichun(School of Electrical and Control Engineering,Liaoning University of Engineering and Technology,Huludao 125105,China;Substation Maintenance Work Area,State Grid Fuxin Power Supply Company,Fuxin 123000,China;Kailuan Group Co,Ltd.,Tangshan 063018,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 [2]国网阜新供电公司变电检修工区,辽宁阜新123000 [3]开滦集团有限责任公司,河北唐山063018

出　　处：《信息与控制》2023年第2期235-244,256,共11页Information and Control

基　　金：国家自然科学基金(51974151,71771111);辽宁省高等学校国(境)外培养项目(2019GJWZD002);辽宁省高等学校创新团队项目(LT2019007);辽宁省教育厅科技项目(LJ2019QL015);辽宁省高等学校基本科研项目(LJKZ0352)。

摘　　要：针对不平衡数据对变压器故障诊断模型辨识精度的影响,提出一种基于自适应综合过采样(ADAptive SYNthetic,ADASYN)与改进鲸鱼算法优化核极限学习机的变压器故障诊断模型。首先,利用ADASYN算法优化变压器故障数据均衡化处理,解决变压器故障数据集类间不平衡给模型带来的偏倚问题。其次,通过多策略组合改进了鲸鱼优化算法(improved whale optimization algorithm,IWOA)的搜索速度、收敛能力和局部极值的逃逸能力。最后,改进鲸鱼算法对核极限学习机(kernel based extreme learning machine,KELM)正则化系数和核函数参数寻优,构建改进鲸鱼算法优化核极限学习机(IWOA-KELM)故障诊断模型。将模型应用于变压器故障诊断领域,用该模型与粒子群算法核极限学习机模型(PSO-KELM)、灰狼算法优化核极限学习机模型(GWO-KELM)和鲸鱼算法核极限学习机模型(WOA-KELM)的诊断精度对比,分别提升14.17%、12.5%和8.34%,这证明了所提故障诊断模型具有更高的精度和泛化能力。In this study,we propose a transformer fault diagnosis model based on adaptive integrated oversampling(ADASYN)and kernel-based extreme learning machine of improved whale algorithm(IWOA-KELM).This model aims to examine the effect of transformer unbalanced data on the recognition accuracy of the transformer fault diagnosis model.The imbalance between transformer fault data sets results in a bias problem.So,to resolve this,we first use the ADASYN algorithm to optimize the equalization process of transformer fault data.Secondly,we use a multi-strategy combination to improve the search speed,convergence ability,and escape ability of local extremums of the whale optimization algorithm(WOA).Finally,the WOA is used to optimize the KELM regularization coefficient and kernel function parameters.It also constructs a fault diagnosis model of the optimized IWOA-KELM.When the model is applied to the field of transformer fault diagnosis,its diagnostic accuracy is found to improve by 14.17%,12.5%,and 8.34%,respectively,for particle swarm algorithm KELM(PSO-KELM),gray wolf algorithm KELM(GWO-KELM),and WOA-KELM.Our findings proved that the proposed fault diagnosis model has higher breaking accuracy and generalization ability.

关 键 词：变压器 油中溶解气体 ADASYN算法 鲸鱼算法 核极限学习机 

分 类 号：TM411[电气工程—电器]