电工电气杂志社

基于分步特征选取和WOA-LSSVM的变压器故障诊断

来源：电工电气发布时间：2024-08-30 13:30 浏览次数：330

基于分步特征选取和WOA-LSSVM的变压器故障诊断

谢乐，杨浙，潘成南

(国网浙江省电力有限公司慈溪市供电公司，浙江慈溪 315300)

摘要：为了提高变压器故障诊断的精度，保障电网的稳定运行，提出了一种基于 ReliefF 算法与界标等距映射(L-Isomap)的分步特征选取和鲸鱼群算法(WOA)优化最小二乘支持向量机(LSSVM)的故障诊断模型。选取 7 种常见故障特征油中溶解气体分析(DGA)气体以及其构造出的16 组比值作为初始特征集，利用 ReliefF 算法分别对初始特征集进行特征选择，再利用 L-Isomap 算法对融合后的特征集进行降维处理，将降维处理后的特征集作为故障特征向量代入诊断模型，故障诊断模型采用 WOA-LSSVM 进行训练与测试。实验结果表明，诊断模型的精度高达98.31%，相比于其他模型拥有更高的诊断精度。

关键词： 变压器；故障诊断；分步特征选取；降维；鲸鱼群算法；最小二乘支持向量机

中图分类号：TM406 ；TM411 文献标识码：B 文章编号：1007-3175(2024)08-0031-06

Transformer Fault Diagnosis Based on Stepwise Feature

Selection and WOA-LSSVM

XIE Le, YANG Zhe, PAN Cheng-nan

（Cixi Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd,Cixi 315300, China）

Abstract: In order to improve the accuracy of transformer fault diagnosis and ensure the stable operation of power system. In this paper,proposing a stepwise feature selection based on ReliefF algorithm and landmark isomap (L-Isomap) and a fault diagnosis model for whale optimization algorithm (WOA) least squares support vector machine (LSSVM). The method first selected 7 common fault characteristics dissolved gas analysis in oil (DGA) gas and constructed 16 sets of ratios as the initial feature set. Secondly, the ReliefF algorithm was used to perform feature selection on the initial feature set respectively, and then the L-Isomap algorithm was used to reduce the dimensionality of the fused feature set, and the dimensionality reduction feature set was substituted into the diagnostic model as a fault feature vector, and the fault diagnosis model was trained and tested by WOA-LSSVM. The experimental results show that the accuracy of the diagnostic model is as high as 98.31%, which is higher diagnostic accuracy than that of other models.

Key words: transformer; fault diagnosis; stepwise feature selection; dimensionality reduction; whale optimization algorithm; least squares support vector machine

参考文献

[1] 刘云鹏，许自强，李刚，等. 人工智能驱动的数据分析技术在电力变压器状态检修中的应用综述[J]. 高电压技术，2019，45(2) ：337-348.

[2] GUARDADAO J L, NAREDO J L, MORENO P， et al.A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis[J].IEEE Transactions on Power Delivery， 2001， 16(4) ：643-647.

[3] DUVAL M, DEPABLA A.Interpretation of gas-inoil analysis using new IEC publication 60599 and IEC TC10 databases[J].IEEE Electrical Insulation Magazine， 2001， 17(2) ：31-41.

[4] KIM Y M, LEE S J, SEO H D, et al.Development of dissolved gas analysis(DGA) expert system using new diagnostic algorithm for oil-immersed transformers[C]//2012 IEEE International Conference on Condition Monitoring and Diagnosis， 2012 ：365-369.

[5] ROGERS R R.IEEE and IEC codes to interpret incipient faults in transformers， using gas in oil analysis[J].IEEE Transactions on Electrical Insulation， 1978，13(5) ：349-354.

[6] MOLLMANN A, PAHLAVANPOUR B.New guidelines for interpretation of dissolved gas analysis in oilfilled transformers[J].Electra， 1999， 186 ：31-51.

[7] LEE S, KIM Y, SEO H, et al.New methods of DGA diagnosis using IEC TC 10 and related databases Part2:Application of relative content of fault gases[J].IEEE Transactions on Dielectrics and Electrical Insulation， 2013， 20(2) ：691-696.

[8] 谢乐. 基于 DGA 和机器学习的变压器故障诊断和状态预测研究[D]. 成都：西南交通大学，2022.

[9] 白星振，臧元，葛磊蛟，等. 变压器故障诊断用油中溶解气体征兆优选方法[J] . 高电压技术，2023，49(9) ：3864-3875.

[10] 谢乐，衡熙丹，刘洋，等. 基于线性判别分析和分步机器学习的变压器故障诊断[J]. 浙江大学学报(工学版)， 2020，54(11) ：2266-2272.

[11] 李云淏，咸日常，张海强，等. 基于改进灰狼算法与最小二乘支持向量机耦合的电力变压器故障诊断方法[J].电网技术，2023，47(4) ：1470-1477.

[12] ZHANG Y, LI X, ZHENG H, et al.A fault diagnosis model of power transformers based on dissolved gas analysis features selection and improved krill herd algorithm optimized support vector machine[J].IEEE Access， 2019， 7 ：102803-102811.

[13] 朱莉，汪小豪，李豪，等. 不平衡样本下基于变异麻雀搜索算法和改进 SMOTE 的变压器故障诊断方法[J].高电压技术，2023，49(12) ：4993-5001.

[14] KONONENKO I.Estimating Attributes:Analysis and Extensions of RELIEF[C]//European Conference on Machine Learning， 1994 ：171-182.

[15] DE SILVA V , TENENBAUM J B . Global versus local methods in nonlinear dimensionality reduction[C]//Neural Information Processing Systems， 2002 ：721-728.

[16] MIRJALILI S, LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software，2016， 95(12) ：51-67.

[17] SUYKENS J A K, VANDEWALLE J.Least squares support vector machine classifiers[J].Neural Processing Letters， 1999， 9(3) ：293-300.

[18] 张又文，冯斌，陈页，等. 基于遗传算法优化 XGBoost 的油浸式变压器故障诊断方法[J] . 电力自动化设备，2021，41(2) ：200-206.

返回索引列表

Article retrieval

文章检索

基于分步特征选取和WOA-LSSVM的变压器故障诊断