Power transformer fault diagnosis method based on AdaBoost.M2-ISSA-ELM algorithm

被引：0

作者：

Wang, Yan ^{[1
]}

Wang, Yinchu ^{[1
]}

Zhao, Hongshan ^{[1
]}

Li, Wei ^{[1
]}

Lian, Hongbo ^{[1
]}

Kang, Lei ^{[1
]}

机构：

[1] School of Electrical and Electronic Engineering, North China Electric Power University, Baoding

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2024年 / 44卷 / 09期

基金：

中国国家自然科学基金;

关键词：

extreme learning machine; fault diagnosis; integrated learning; intelligent optimization algorithm; power transformers;

D O I：

10.16081/j.epae.202403025

中图分类号：

学科分类号：

摘要：

In order to improve the accuracy of power transformer fault diagnosis，a power transformer fault diagnosis method is proposed by combining ensemble learning and swarm intelligence optimization algorithm. Using the extreme learning machine（ELM） as the basic learning algorithm to construct the base classifier under the integrated learning framework，and aiming at the problem that the performance of the ELM model is greatly affected by parameter initialization and is prone to fall into local optimization，an improved sparrow search algorithm（ISSA） based on sinusoidal optimization is introduced to optimize relevant parameters，and improve the classification performance of the basic classifier. Then，an improved adaptive boosting（AdaBoost.M2） algorithm is used to build an ensemble learning model，expand the output of the base classifier，and the pseudo loss function is introduced to replace the weighted error in the traditional AdaBoost algorithm to enhance the comprehensive expression ability of the integrated classifier. A power transformer fault diagnosis model based on AdaBoost.M2-ISSA-ELM algorithm is obtained，which further improves the recognition accuracy of the model. The proposed method is analyzed through 909 sets of dissolved gases analysis（DGA） samples，and the results show that the method has good diagnostic accuracy and classification performance，and can achieve accurate identification of power transformer fault types. © 2024 Electric Power Automation Equipment Press. All rights reserved.

引用

页码：205 / 211and218

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