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| Transformer fault diagnosis based on linear discriminant analysis and step-by-step machine learning |
Le XIE( ),Xi-dan HENG,Yang LIU,Qi-long JIANG,Dong LIU*() |
| School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China |
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Abstract A feature selection method based on the linear discriminant analysis (LDA) was proposed, and a diagnostic model based on the step-by-step machine learning was established, in order to improve the current shortcomings of transformer fault diagnosis in feature selection and single diagnosis model, as well as the accuracy and the efficiency of transformer fault diagnosis. The multi-characteristic parameters of volume fraction ratio of dissolved gas in 16 groups of oil were selected by this model. And the linear discriminant analysis was performed to apply dimensionality reduction on parameters and the results were used as input eigenvectors. Then the probabilistic neural networks were used to diagnose transformer faults and to distinguish confusing faults. The confusing fault was further distinguished by the support vector machine optimized by the grey wolf swarm algorithm.The final experimental diagnostic accuracy rate was 97.27%, while the diagnostic time was 4.87 s. The proposed model not only has higher accuracy, but also has better efficiency, compared with a single machine learning model. Case analysis shows that this method can make up for the shortcoming of single machine learning, which can provide reference for the fault diagnosis of power transformer with limited fault cases.
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Received: 20 November 2019
Published: 15 December 2020
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Corresponding Authors:
Dong LIU
E-mail: leohfut@126.com;liudong@swjtu.edu.cn
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基于线性判别分析和分步机器学习的变压器故障诊断
为了改善当前变压器故障诊断在特征量选取和使用单一诊断模型进行故障诊断上的不足，提高变压器故障诊断的准确率和效率，提出基于线性判别分析（LDA）的特征选取方法，建立基于分步机器学习的诊断模型. 该模型选取16组油中溶解气体体积分数比值的多特征参数，运用线性判别分析对参数进行降维作为输入特征向量；运用概率神经网络对变压器故障做出初步诊断，区分出易混淆故障；使用基于灰狼群算法优化的支持向量机对易混淆故障做进一步的区分. 最终实验诊断准确率为97.27%，诊断时间为4.87 s. 与单一机器学习模型相比，所提出的模型不仅具有更高的准确率，还具有更高的效率. 实例分析表明，本研究方法能有效弥补单一机器学习的缺陷，为故障样本有限情况下的电力变压器故障诊断提供参考.
关键词：
变压器,
故障诊断,
机器学习,
特征参数,
线性判别分析(LDA)
|
|
| [1] |
汪永华. 常用电气与电控设备故障诊断400例 [M]. 北京: 中国电力出版社, 2011.
|
|
|
| [2] |
ELDIN A A H, REFAEY M A A novel algorithm for discrimination between inrush current and internal faults in power transformer differential protection based on discrete wavelet transform[J]. Electric Power Systems Research, 2011, 81 (1): 19- 24
doi: 10.1016/j.jpgr.2010.07.010
|
|
|
| [3] |
夏飞, 罗志疆, 张浩, 等 混合神经网络在变压器故障诊断中的应用[J]. 电子测量与仪器学报, 2017, 31 (1): 118- 124
XIA Fei, LUO Zhi-jiang, ZHANG Hao, et al Application of hybrid neural network in transformer fault diagnosis[J]. Journal of Electronic Measurement and Instrument, 2017, 31 (1): 118- 124
|
|
|
| [4] |
公茂法, 张言攀, 柳岩妮, 等 基于 BP 网络算法优化模糊Petri 网的电力变压器故障诊断[J]. 电力系统保护与控制, 2015, 43 (3): 113- 117
GONG Mao-fa, ZHANG Yan-pan, LIU Yan-ni, et al Power transformer fault diagnosis based on BP neural network optimized fuzzy petri net[J]. Power System Protection and Control, 2015, 43 (3): 113- 117
doi: 10.7667/j.issn.1674-3415.2015.03.017
|
|
|
| [5] |
薛浩然, 张珂珩, 李斌, 等 基于布谷鸟算法和支持向量机的变压器故障诊断[J]. 电力系统保护与控制, 2015, 43 (8): 8- 13
XUE Hao-ran, ZHANG Ke-heng, LI Bin, et al Transformer fault diagnosis based on cuckoo algorithm and support vector machine[J]. Power System Protection and Control, 2015, 43 (8): 8- 13
doi: 10.7667/j.issn.1674-3415.2015.08.002
|
|
|
| [6] |
张耀鹏. 基于决策树算法的扼流适配变压器故障诊断系统研究与设计[D]. 北京: 北京交通大学, 2018.
ZHANG Yao-peng. Research and design of fault diagnosis system for turbulence adaptive transformer based on decision tree algorithm [D]. Beijing: Beijing Jiaotong University, 2018.
|
|
|
| [7] |
吐松江·卡日, 高文胜, 张紫薇, 等 基于支持向量机和遗传算法的变压器故障诊断[J]. 清华大学学报: 自然科学版, 2018, 58 (7): 623- 629
TU Song-jiang·KA Ri, GAO Wen-sheng, ZHANG Zi-wei, et al Transformer fault diagnosis based on support vector machine and genetic algorithm[J]. Journal of Tsinghua University: Science and Technology, 2018, 58 (7): 623- 629
|
|
|
| [8] |
汪可, 李金忠, 张书琦, 等 变压器故障诊断用油中溶解气体新特征参量[J]. 中国电机工程学报, 2016, 36 (23): 6570- 6578
WANG Ke, LI Jin-zhong, ZHANG Shu-qi, et al New characteristic parameters of dissolved gases in oil for transformer fault diagnosis[J]. Proceedings of the CSEE, 2016, 36 (23): 6570- 6578
|
|
|
| [9] |
高杰. 基于优化PNN网络的变压器故障诊断研究[D]. 淮南: 安徽理工大学, 2018.
GAO Jie. Research on transformer fault diagnosis based on optimized PNN network [D]. Huainan: Anhui University of Science and Technology, 2018.
|
|
|
| [10] |
YANG X, CHEN W, LI A, et al BA-PNN-based methods for power transformer fault diagnosis[J]. Advanced Engineering Informatics, 2019, 39: 178- 185
doi: 10.1016/j.aei.2019.01.001
|
|
|
| [11] |
吴广宁, 袁海满, 宋臻杰, 等 基于粗糙集与多类支持向量机的电力变压器故障诊断[J]. 高电压技术, 2017, 43 (11): 3668- 3674
WU Guang-ning, YUAN Hai-man, SONG Zhen-jie, et al Fault diagnosis of power transformer based on rough set and multi-class support vector machine[J]. High Voltage Engineering, 2017, 43 (11): 3668- 3674
|
|
|
| [12] |
阳艳 电力变压器常见故障分析与检测[J]. 中国科技博览, 2015, (26): 278
YANG Yan Analysis and detection of common faults in power transformers[J]. China Science and Technology Expo, 2015, (26): 278
|
|
|
| [13] |
DUVAL M, DEPABLA A Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases[J]. IEEE Electrical Insulation Magazine, 2001, 17 (2): 31- 41
doi: 10.1109/57.917529
|
|
|
| [14] |
BAKAR N A, ABU-SIADA A, ISLAM S A review of dissolved gas analysis measurement and interpretation techniques[J]. IEEE Electrical Insulation Magazine, 2014, 30 (3): 39- 49
doi: 10.1109/MEI.2014.6804740
|
|
|
| [15] |
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, (5): 349- 354
|
|
|
| [16] |
王学磊, 李庆民, 杨芮, 等 基于油色谱分析的变压器复合绝缘缺陷多指标综合权重评估方法[J]. 高电压技术, 2015, 41 (11): 3836- 3842
WANG Xue-lei, LI Qing-min, YANG Rui, et al Multi-index comprehensive weight evaluation method for transformer composite insulation defects based on oil chromatography[J]. High Voltage Engineering, 2015, 41 (11): 3836- 3842
|
|
|
| [17] |
李春茂, 周妺末, 刘亚婕, 等 基于邻域粗糙集与多核支持向量机的变压器多级故障诊断[J]. 高电压技术, 2018, 44 (11): 3474- 3482
LI Chun-mao, ZHOU Mo-mo, LIU Ya-jie, et al Multi-level fault diagnosis of transformer based on neighborhood rough set and multi-core support vector machine[J]. High Voltage Technology, 2018, 44 (11): 3474- 3482
|
|
|
| [18] |
周志华. 机器学习 [M]. 北京: 清华大学出版社, 2016: 230-232.
|
|
|
| [19] |
LI M, YUAN B 2D-LDA: astatistical linear discriminant analysis for image matrix[J]. Pattern Recognition Letters, 2005, 26 (5): 527- 532
doi: 10.1016/j.patrec.2004.09.007
|
|
|
| [20] |
SPECHT D F Probabilistic neural networks[J]. Neural Networks, 1990, 3 (1): 109- 118
doi: 10.1016/0893-6080(90)90049-Q
|
|
|
| [21] |
HEARST M A, DUMAIS S T, OSUUA E, et al Support vector machines[J]. IEEE Intelligent Systems and Their Applications, 1998, 13 (4): 18- 28
doi: 10.1109/5254.708428
|
|
|
| [22] |
魏政磊, 赵辉, 李牧东, 等 控制参数值非线性调整策略的灰狼优化算法[J]. 空军工程大学学报: 自然科学版, 2016, 17 (3): 104- 110
WEI Zheng-lei, ZHAO Hui, LI Mu-dong, et al Grey wolf optimization algorithm for controlling parameter value nonlinear adjustment strategy[J]. Journal of Air Force Engineering University, 2016, 17 (3): 104- 110
|
|
|
| [23] |
IEC. Mineral oil-impregnated electrical equipment in service-guide to the interpretation of dissolved and free gases analysis: EN 60599-2007 [S]. Geneva: IEC/CEI, 2007.
|
|
|
|
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