Fault Diagnosis Study of Oil-Immersed Transformers of Optimized Kernel Based Extreme Learning Machine Based on Modified Aquila Optimizer

YI Lingzhi; LONG Jiao; WANG Yahui; HUANG Jianxiong; SUN Tao; YU Huang; YU Guo

doi:10.3969/j.issn.1008-0198.2022.06.002

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Fault Diagnosis Study of Oil-Immersed Transformers of Optimized Kernel Based Extreme Learning Machine Based on Modified Aquila Optimizer

更新时间：2025-06-11

- Fault Diagnosis Study of Oil-Immersed Transformers of Optimized Kernel Based Extreme Learning Machine Based on Modified Aquila Optimizer
- Vol. 42, Issue 6, Pages: 7-14(2022)
- 作者机构：
  
  1. 湘潭大学自动化与电子信息学院,湖南,湘潭,411105
  2. 湖南省多能源协同控制技术工程研究中心,湖南,湘潭,411105
  3. 湖南大学电气与信息工程学院,湖南,长沙,410082
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2022.06.002
  CLC： TM411
- Published：2022
- 稿件说明：
移动端阅览
易灵芝, 龙娇, 王雅慧, 黄健雄, 孙涛, 余煌, 禹果. 基于改进天鹰算法优化核极限学习机的油浸式变压器故障诊断研究[J]. 湖南电力, 2022, 42(6): 7-14.

YI Lingzhi, LONG Jiao, WANG Yahui, et al. Fault Diagnosis Study of Oil-Immersed Transformers of Optimized Kernel Based Extreme Learning Machine Based on Modified Aquila Optimizer[J]. 2022, 42(6): 7-14.
易灵芝, 龙娇, 王雅慧, 黄健雄, 孙涛, 余煌, 禹果. 基于改进天鹰算法优化核极限学习机的油浸式变压器故障诊断研究[J]. 湖南电力, 2022, 42(6): 7-14. DOI： 10.3969/j.issn.1008-0198.2022.06.002.

YI Lingzhi, LONG Jiao, WANG Yahui, et al. Fault Diagnosis Study of Oil-Immersed Transformers of Optimized Kernel Based Extreme Learning Machine Based on Modified Aquila Optimizer[J]. 2022, 42(6): 7-14. DOI： 10.3969/j.issn.1008-0198.2022.06.002.

摘要

为提高油浸式电力变压器故障诊断的精度及可靠性

提出了一种基于改进天鹰算法(modified aquila optimizer

MAO)优化核极限学习机(kernel based extreme learning machine

KELM)的油浸式电力变压器故障诊断方法。利用Tent混沌映射、卡方概率密度函数

对原始天鹰优化算法(Aquila Optimizer

AO)进行改进

改进后的算法有效提升了收敛速度与寻优精度。利用MAO算法对核极限学习机模型中的正则化系数和核函数参数进行联合寻优

构建最优故障诊断模型。实验结果显示

MAO-KELM对变压器故障诊断的准确率达到95.8%

比AO、GWO和PSO优化的核极限学习机故障诊断模型分别提升了3.52%、10.07%和11.64%

体现了MAO算法的优越性

同时与传统模型进行比较

证明所提方法的诊断效果具有明显优势。

Abstract

In order to improve the accuracy and reliability of oil-immersed power transformer fault diagnosis

an oil-immersed power transformer fault diagnosis method of optimized kernel based extreme learning machine (KELM)based on the modified aquila optimizer (MAO)is proposed in this paper. The original aquila optimizer (AO) is improved by using Tent chaotic mapping and cardinality probability density function

and the improved algorithm effectively improves the convergence speed and the accuracy of the optimization search.The optimal fault diagnosis model is constructed by jointly seeking the regularization coefficients and kernel function parameters in the KELM using the MAO algorithm. The experimental results show that the accuracy of MAO-KELM for transformer fault diagnosis reaches 95.8%

which is 3.52%

10.07% and 11.64% higher than the optimized KELM fault diagnosis models of AO

GWO and PSO respectively

reflecting the superiority of the MAO algorithm

and comparing with the traditional model

the proposed method has obvious advantages in its diagnosis effect.

关键词

Keywords

references

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