基于小波变换与逻辑斯蒂回归的混合式配电变压器故障辨识

doi:10.19595/j.cnki.1000-6753.tces.L90178

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Abstract Hybrid distribution transformer (HDT) can replace traditional distribution transformers in intelligent distribution networks to achieve reactive power compensation, harmonic control, voltage regulation and other functions. In order to distinguish between the internal fault of the transformer and the power electronic fault when the HDT fails, this paper first obtains a large amount of current characteristic data of primary side, secondary side, tertiary side and quaternary side under different fault conditions of HDT through simulation. Then, with the help of wavelet transform theory, four-layer discrete wavelet transform is performed on the obtained data and the normalized energy, the normalized energy moment and sample entropy of the data in the wavelet domain are used as the eigenvalues of the current characteristic data. Using machine learning technology, a logistic regression classifier is constructed, and the feature matrix composed of feature values is used as the input of the classifier, and the model is trained to obtain a classifier model with good receiver operating characteristic (ROC) curve and confusion matrix performance. Finally, the data was randomly extracted many times, and the accuracy of the HDT fault recognition of the machine learning model obtained by training was about 90%.

Key words： Hybrid distribution transformer (HDT) wavelet transform machine learning fault identification

Received: 30 June 2020

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TM421

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	Articles by authors
	Zhang Lishi
	Liang Deliang
	Liu Hua
	Liu Yibin
	Li Dawei

Cite this article:

Zhang Lishi,Liang Deliang,Liu Hua等. Fault Identification of Hybrid Distribution Transformer Based on Wavelet Transform and Logistic Regression[J]. Transactions of China Electrotechnical Society, 2021, 36(zk2): 467-476.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L90178 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/Izk2/467

[1] 陶顺, 陈鹏伟, 肖湘宁, 等. 智能配电网不确定性建模与供电特征优化技术综述[J]. 电工技术学报, 2017, 32(10): 77-91.
Tao Shun, Chen Pengwei, Xiao Xiangning, et al.Review on uncertainty modeling and power supply characteristics optimization technology in smart distribution network[J]. Transactions of China Electrotechnical Society, 2017, 32(10): 77-91
[2] 宋强, 赵彪, 刘文华, 等. 智能直流配电网研究综述[J]. 中国电机工程学报, 2013, 33(25): 9-19.
Song Qiang, Zhao Biao, Liu Wenhua, et al.An overview of research on smart DC distribution power network[J]. Proceedings of the CSEE, 2013, 33(25): 9-19.
[3] Lai J S, Mansoor A, Maitra A, et al. Multifunction hybrid intelligent universal transformer: US, 6954366[P].2005-10-11.
[4] Haj-M, Bala S, Tang L. Hybrid distribution transformer with an integrated voltage source converter: US, 20100220499A1[P].2010-02-26.
[5] EL Refaie A M. High speed operation of permanent magnet machines[D]. Madsion, USA: The University of Wisconsin-Madsion, 2005.
[6] Sastry J, Bala S.Considerations for the design of power electronic modules for hybrid distribution transformers[C]//Proceedings of 2013 IEEE Energy Conversion Congress and Exposition, Denver, CO, USA, 2013: 1422-1428.
[7] 高荣, 寇鹏, 梁得亮, 等. 含混合式配电变压器的主动配电网电压鲁棒模型预测控制[J]. 中国电机工程学报, 2020, 40(7): 2081-2090, 2388.
Gao Rong, Kou Peng, Liang Deliang, et al.Robust model predictive control for the voltage regulation in active distribution networks with hybrid distribution transformers[J]. Proceedings of the CSEE, 2020, 40(7): 2081-2090, 2388.
[8] 李宗博, 焦在滨, 何安阳. 基于等效磁化曲线智能识别的变压器保护原理[J]. 电工技术学报, 2020, 35(7): 1464-1475.
Li Zongbo, Jiao Zaibin, He Anyang.Equivalent magnetization curve intelligent recognition based transformer protection[J]. Transactions of China Electrotechnical Society, 2020, 35(7): 1464-1475.
[9] 周念成, 李春艳, 王强钢. 基于多变量多尺度熵的变压器励磁涌流识别方法[J]. 电工技术学报, 2018, 33(15): 3426-3436.
Zhou Niancheng, Li Chunyan, Wang Qianggang.An algorithm to identify transformer inrush currents based on multivariate multiscale sample entropy[J]. Transa-ctions of China Electrotechnical Society, 2018, 33(15): 3426-3436.
[10] 李臻, 罗林根, 盛戈皞, 等. 基于压缩感知的特高频局部放电定位法[J]. 电工技术学报, 2018, 33(1): 202-208.
Li Zhen, Luo Lingen, Sheng Geao, et al.Ultrahigh frequency partial discharge localization metho-dology based on compressed sensing[J]. Transactions of China Electrotechnical Society, 2018, 33(1): 202-208.
[11] 丁晓兵, 周红阳, 黄佳胤, 等. 基于逻辑斯蒂回归的变压器涌流识别[J]. 电力系统及其自动化学报, 2020, 32(12): 77-84, 94.
Ding Xiaobing, Zhou Hongyang, Huang Jiayin, et al.Transformer inrush current identification based on logistic regression[J]. Proceedings of the CSU-EPSA, 2020, 32(12): 77-84, 94.
[12] Mohamed E A, Abdelaziz A Y, Mostafa A S.A neural network-based scheme for fault diagnosis of power transformers[J]. Electric Power Systems Research, 2005, 75(1): 29-39.
[13] 黄永红, 马锋, 沈敏. 基于最小二乘支持向量机的变压器励磁涌流识别方法研究[J]. 电力系统保护与控制, 2010, 38(23): 93-96.
Huang Yonghong, Ma Feng, Shen Min.Research of magnetizing inrush current identification method based on LS-SVM[J]. Power System Protection and Control, 2010, 38(23): 93-96.
[14] 梁得亮, 柳轶彬, 寇鹏, 等. 智能配电变压器发展趋势分析[J]. 电力系统自动化, 2020, 44(7): 1-18.
Liang Deliang, Liu Yibin, Kou Peng, et al.Analysis of developing trend for intelligent distribution transformer[J]. Automation of Electric Power Systems 2020, 44(7): 1-18.
[15] Liu Yibin, Liang Deliang, Liang Yang, et al.Design and analysis of the compounded control system of hybrid distribution transformer[C]//2018 IEEE Energy Conversion Congress and Exposition (ECCE), Portland, OR, USA, 2018: 23-27.