Transformer Fault Diagnosis Method Based on Data Equalization and Kernel-based Extreme Learning Machine of Improved Whale Algorithm
WANG Yuhong1, SUN Yuanxing1, BAO Weichuan2, CHEN Zichun1,3
1. School of Electrical and Control Engineering, Liaoning University of Engineering and Technology, Huludao 125105, China; 2. Substation Maintenance Work Area, State Grid Fuxin Power Supply Company, Fuxin 123000, China; 3. Kailuan Group Co, Ltd., Tangshan 063018, China
Abstract：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.
王雨虹, 孙远星, 包伟川, 陈子春. 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[J]. 信息与控制, 2023, 52(2): 235-244,256.
WANG Yuhong, SUN Yuanxing, BAO Weichuan, CHEN Zichun. Transformer Fault Diagnosis Method Based on Data Equalization and Kernel-based Extreme Learning Machine of Improved Whale Algorithm. Information and control, 2023, 52(2): 235-244,256.
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