Quality Detection，Diagnosis and Testing of Power Transformer Based on Chaotic Algorithm

TIAN Lin; ZHANG Da; LIU Zhen; WU Hongbo; YAN Jing

doi:10.13296/j.1001-1609.hva.2025.06.015

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Quality Detection，Diagnosis and Testing of Power Transformer Based on Chaotic Algorithm

Research & Analysis | 更新时间：2025-06-12

- Quality Detection，Diagnosis and Testing of Power Transformer Based on Chaotic Algorithm
- High Voltage Apparatus Vol. 61, Issue 6, Pages: 131-137(2025)
- 作者机构：
  
  1.国网河北省电力有限公司，石家庄　 050000
  2.国家电网河北省营销服务中心，石家庄　 050000
  3.武汉大学，武汉　 430000
- 作者简介：
- 基金信息：
  
  State Grid Hebei Electric Power Co., Ltd. Technology Project(KJ2021-018)
- DOI：10.13296/j.1001-1609.hva.2025.06.015
  CLC：
- Received：10 December 2024，
  
  Revised：10 March 2025，
  
  Published：16 June 2025
- 稿件说明：
移动端阅览
TIAN Lin, ZHANG Da, LIU Zhen, et al. Quality Detection，Diagnosis and Testing of Power Transformer Based on Chaotic Algorithm[J]. High voltage apparatus, 2025, 61(6): 131-137.
DOI：

TIAN Lin, ZHANG Da, LIU Zhen, et al. Quality Detection，Diagnosis and Testing of Power Transformer Based on Chaotic Algorithm[J]. High voltage apparatus, 2025, 61(6): 131-137. DOI： 10.13296/j.1001-1609.hva.2025.06.015.

摘要

变压器是电力供应的重要组成部分，油中溶解气体测试方法通常用于配电变压器的质量检测，从而诊断其故障。文中提出了一种改进的优化算法，利用溶解气体分析（disslved gases analysis，DGA）对电力变压器质量检测从而进行故障预测。首先，从各种来源收集来自变压器DGA的数据，并选择最佳混合特征集作为模型的输入。其次，对于特征选择，采用核主成分分析（kernel principal component analysis，KPCA），为了优化最小二乘支持向量机（least squares support vector machine，LSSVM）模型并构建质量检测诊断模型，提出了一种结合混沌阿基米德优化算法（chaotic archimedes optimization algorithm，CAOA）和LSSVM的混合优化算法。最后，将所提CAOA、其他模型及建议的模型进行对比，测试结果表明所提CAOA在检测变压器故障方面比其他传统方法更准确。

Abstract

Transformer is an important component of power supply

and the testing method of dissolved gas in oil is usually used for quality inspection of distribution transformer so to diagnose its faults. In this paper

an improved optimization algorithm is proposed and the dissolved gases analysis (DGA) is used for quality detection of power trans-former and thus for fault prediction. Firstly

the DGA data from of transformer is collected from various sources and the best mixed feature set is selected as the input to the model. For feature selection

the kernel principal component analysis (KPCA) is used. For optimizing the least squares support vector machine (LSSVM) model and constructing a quality detection diagnostic model

a hybrid optimization algorithm combining chaotic archimedes optimization algorithm (CAOA) and LSSVM is proposed

and the proposed CAOA and other models as well as the suggested model are compared. The test results show that the proposed CAOA is more accurate than other traditional methods in detecting the fault of transformer.

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