Transformer fault diagnosis based on a multi-strategy improved dung beetle optimizer

被引：0

作者：

Zhao X. ^{[1
]}

Wang D. ^{[1
]}

Peng H. ^{[2
]}

Yu H. ^{[1
]}

Li S. ^{[2
]}

机构：

[1] Shenyang Institute of Technology, Shenyang

[2] Liaoning Technical University, Huludao

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2024年 / 52卷 / 06期

基金：

中国国家自然科学基金;

关键词：

bidirectional long short-term memory network; fault diagnosis; improved dung beetle optimizer; KPCA; transformer;

D O I：

10.19783/j.cnki.pspc.230783

中图分类号：

学科分类号：

摘要：

To ensure reliable oil-immersed transformer fault diagnosis, a transformer fault diagnosis method based on a multi-strategy improved dung beetle optimizer (MIDBO) optimized bi-directional long short-term memory network (BiLSTM) is proposed. There are shortcomings of the dung beetle algorithm, such as poor global search ability and it is easy for it to fall into a local optimum. First, through the Bernoulli chaotic map, the introduction of adaptive factors and the Levy flight strategy, the dynamic weight coefficient is improved and its performance is evaluated. Then, MIDBO is used to optimize many hyperparameters of BiLSTM to form the MIDBO-BiLSTM fault diagnosis model. Kernel principal component analysis (KPCA) is used to extract the eigenvalues, and the correlation between the eigenvalues and the fault types is analyzed in depth to improve the convergence speed of the model. The final experimental results show that the proposed MIDBO-BiLSTM transformer fault diagnosis method has high accuracy and strong generalizability. Its accuracy rate is as high as 94.67%. This is suitable for transformer fault diagnosis. © 2024 Power System Protection and Control Press. All rights reserved.

引用

页码：120 / 130

页数：10

共 25 条

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