Transformer fault diagnosis based on DGA and a whale algorithm optimizing a LogitBoost-decision tree

被引：0
|
作者：
Zhang G. [1 ]
Chen K. [1 ]
Fang R. [1 ]
Wang K. [1 ]
Zhang X. [1 ]
机构：
[1] Hubei Engineering Research Center for New Energy and Power Grid Equipment Safety Monitoring, Hubei University of Technology, Wuhan
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2023年 / 51卷 / 07期
基金：
中国国家自然科学基金;
关键词：
decision tree; dissolved gas in oil; fault diagnosis; LogitBoost; transformer; whale optimization algorithm;
D O I：
10.19783/j.cnki.pspc.220895
中图分类号：
学科分类号：
摘要：
To diagnose a transformer fault accurately, dissolved gas analysis (DGA) is combined with artificial intelligence technology, such that a transformer fault diagnosis model based on LogitBoost-decision tree optimized by whale optimization algorithm (WOA) can be obtained. The model takes the decision tree as the weak learner. The LogitBoost ensemble algorithm is used as an integration framework to integrate multiple decision trees into a strong learner. A model optimization strategy based on the whale optimization algorithm is constructed to optimize the decision tree and the maximum splitting times of the decision tree in the LogitBoost-decision tree model. The experiments show that the WOA-LogitBoost-DT transformer diagnosis model improves the comprehensive diagnosis accuracy by about 4%, 10% and 21% compared with the commonly used decision tree, support vector machine and three ratio diagnosis models, respectively. The proposed model can provide technical support for transformer fault diagnosis. © 2023 Power System Protection and Control Press. All rights reserved.
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页码：63 / 72
页数：9
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