相对变换主元分析特征提取的变压器故障诊断

doi:10.16182/j.issn1004731x.joss.201803045

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (3): 1127-1134.doi: 10.16182/j.issn1004731x.joss.201803045

相对变换主元分析特征提取的变压器故障诊断

唐勇波^1,2, 熊印国¹

1.宜春学院物理科学与工程技术学院,宜春 336000;
2.江西工程学院电子信息工程学院,新余 338000

收稿日期:2016-04-05 出版日期:2018-03-08 发布日期:2019-01-02
作者简介:唐勇波(1975-),男,湖南永州,博士,副教授,研究方向为智能控制和故障诊断技术等;熊印国(1974-),男,江西丰城,硕士,讲师,研究方向为复杂工业过程建模,故障诊断等。
基金资助:
江西省教育厅科技项目(GJJ161015)

Transformer Fault Diagnosis Based on Feature Extraction of Relative Transformation Principal Component Analysis

Tang Yongbo^1,2, Xiong Yinguo¹

1.School of Physical Science and Engineering, Yichun University, Yichun 336000, China;
2.School of Electronic Information Engineering, Jiangxi University of Engineering, Xinyu 338000, China

Received:2016-04-05 Online:2018-03-08 Published:2019-01-02

摘要/Abstract

摘要： 针对主元分析提取变压器油中溶解气体故障特征不明显问题,提出基于相对变换(RT)主元分析(PCA)的变压器故障诊断方法。采用相对变换将原始数据空间变换到相对空间,提高数据之间的可区分性;利用主元分析来降低相对空间维数,使提取的主元特征更具有代表性;根据故障特征,建立基于最小二乘支持向量机(LSSVM)的变压器故障诊断模型并采用混沌粒子群算法对核参数进行优化。结果表明,相对变换主元分析能够有效提取油中溶解气体故障特征,提高数据集的可分性,相比于PCA-LSSVM、RT-LSSVM和灰关联熵方法,所提出的方法具有较优的故障诊断能力。

关键词: 变压器, 故障诊断, 相对变换, 主元分析, 最小二乘支持向量机, 粒子群算法

Abstract: In order to handle the problem that the feature extraction of dissolved gas analysis (DGA) data by principal component analysis (PCA) is not distinct, a new transformer fault diagnosis method based on relative transformation (RT) PCA is proposed. The original data space is converted to the relative data space by relative transformation which makes the transformed data more distinguishable. PCA is employed to reduce the dimension of relative space to make the features more representative in the relative space. Diagnosis model based on least squares support vector machine (LSSVM) is set up according to the fault characteristic of transformer. The chaos particle swarm optimization (CPSO) algorithm is adopted to optimize the kernel parameters of LSSVM. Compared with PCA-LSSVM, RT-LSSVM and grey relation entropy, experimental results show that RTPCA increases the separability of data set and verify the fault diagnosis ability of the proposed method.

Key words: transformer, fault diagnosis, relative transformation, principal component analysis, least squares support vector machine, particle swarm optimization algorithm

中图分类号:

唐勇波, 熊印国. 相对变换主元分析特征提取的变压器故障诊断[J]. 系统仿真学报, 2018, 30(3): 1127-1134.

Tang Yongbo, Xiong Yinguo. Transformer Fault Diagnosis Based on Feature Extraction of Relative Transformation Principal Component Analysis[J]. Journal of System Simulation, 2018, 30(3): 1127-1134.

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相对变换主元分析特征提取的变压器故障诊断

Transformer Fault Diagnosis Based on Feature Extraction of Relative Transformation Principal Component Analysis

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