Fault Diagnosis Model of Transformer Based on GJO Feature Optimization and AO-RF

YE Yulin; LIU Sen; HUANG Song; HAN Xiaohui; DU Zhenbin; LI Bin; LYU Jie; XUE Yang; ZHAO Chunlin

doi:10.13296/j.1001-1609.hva.2024.05.013

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Fault Diagnosis Model of Transformer Based on GJO Feature Optimization and AO-RF

Research & Analysis | 更新时间：2025-04-26

- Fault Diagnosis Model of Transformer Based on GJO Feature Optimization and AO-RF
- High Voltage Apparatus Vol. 60, Issue 5, Pages: 99-107(2024)
- 作者机构：
  
  1.中广核工程有限公司，广东　深圳　 518124
  2.河北科技大学电气工程学院，石家庄　 050018
  3.保定天威保变电气股份有限公司河北省输变电装备电磁与结构性能重点实验室，河北　保定　 071056
  4.保定天威新域科技发展有限公司，河北　保定　 071056
  5.河北卫讯电力自动化设备有限公司，河北　衡水　 053000
- 作者简介：
- 基金信息：
  
  S & T Program of Hebei(20312101D)
- DOI：10.13296/j.1001-1609.hva.2024.05.013
  CLC：
- Received：16 December 2023，
  
  Revised：17 February 2024，
  
  Published：16 May 2024
- 稿件说明：
移动端阅览
YE Yulin, LIU Sen, HUANG Song, et al. Fault Diagnosis Model of Transformer Based on GJO Feature Optimization and AO-RF[J]. High voltage apparatus, 2024, 60(5): 99-107.
DOI：

YE Yulin, LIU Sen, HUANG Song, et al. Fault Diagnosis Model of Transformer Based on GJO Feature Optimization and AO-RF[J]. High voltage apparatus, 2024, 60(5): 99-107. DOI： 10.13296/j.1001-1609.hva.2024.05.013.

摘要

在变压器故障诊断过程中，进行合理的特征优选，将有助于提高诊断模型的诊断精度，为此，文中提出了一种基于金豺优化算法（golden Jackal optimization，GJO）特征量优选与AO-RF的变压器故障诊断模型。首先，采用GJO对构建的21维变压器油中溶解气体特征量进行优选；然后，根据GJO得到的特征优选结果，采用天鹰算法（aquila optimizer，AO）优化随机森林（random forest，RF）的变压器故障诊断模型对变压器故障进行诊断，并与不同特征量、不同故障诊断模型的诊断结果进行了对比。实验结果表明：GJO优选特征量相比21维原始特征、三比值法、无编码比值法以及AO优选特征量的故障诊断准确率可提高1.12%~25.78%，kappa系数可提高0.02~0.24；AO-RF故障诊断模型较RF、SVM、ELM、SSA-RF、WOA-RF、GJO-RF模型的诊断准确率可提高1.84%~15.86%，kappa系数可提高0.02~0.16，验证了所提方法的有效性和准确性。

Abstract

In the fault diagnosis process of transformer

reasonable feature selection is contribute to improve the diagnostic accuracy of the diagnosis model. For that

a fault diagnosis model of transformer based on GJO feature optimization and AO-RF is proposed in this paper. Firstly

the golden Jackal optimization (GJO) algorithm is used to optimize the characteristic quantity of 21-d transformer dissolved gas-in-oil; Then

according to the feature optimization results obtained by GJO

the fault diagnosis model of transformer with random forest (RF) optimized by the aquila optimization (AO) algorithm is used to diagnose the fault of transformer. And it is compared with the diagnosis results that have different feature parameters and different fault diagnosis models. The experimental result shows that the fault diagnosis accuracy of the GJO optimized feature

compared with 21-d original feature

three-ratio method

non-coded ratio method and AO optimized feature

can be improved by 1.12%-25.78%

and the Kappa coefficient can be increased by 0.02-0.24; The fault diagnosis accuracy of AO-RF

compared with RF

SVM

ELM

SSA-RF

WOA-RF and GJO-RF models

can be increased by 1.84%-15.86%

and the Kappa coefficient can be increased by 0.02-0.16

which verify the effectiveness and accuracy of the proposed method.

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