Prediction of UHV Shunt Reactor Top Oil Temperature Based on Optimal Segmentation and Improved Semi-physical Model

被引：0
作者：
Tan F. [1 ]
Chen H. [1 ]
He J. [2 ]
机构：
[1] Maintenance Branch Company, State Grid Jiangsu Electric Power Co., Ltd., Nanjing
[2] School of Electrical Engineering, Southeast University, Nanjing
来源：
Dianwang Jishu/Power System Technology | 2021年 / 45卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Elman neural network; Forecast; K-means clustering; Semi- physical model; Top oil temperature; UHV shunt reactor; Validity function;
D O I：
10.13335/j.1000-3673.pst.2020.0861
中图分类号：
学科分类号：
摘要：
A top oil temperature prediction method for UHV shunt reactor based on optimal segmentation and improved semi-physical model is proposed to effectively evaluate the internal thermal state of the UHV shunt reactor. Firstly, based on fully studying the "sinusoidal" change trend of the top oil temperature curve of the UHV Shunt reactor, the top oil temperature curve is segmented by the K-means clustering method. Then the validity function is established based on the intra-segment distance, the inter-segment distance and the overlap degree, and the selection of the optimal number of top oil temperature curves is realized. Finally, after comprehensively considering the discretization error of the semi-physical forecasting model of the transformer top oil temperature and the main influencing factors of the UHV shunt reactor, an improved semi-physical model suitable for the prediction of the UHV shunt reactor top oil temperature is proposed, and the Elman neural network is used to forecast the UHV shunt reactor top oil temperature in East China. The results show that the average forecasting error of the method proposed is relatively high with the forecasting accuracy of 1.00%, which can meet the accuracy requirements of the actual application in the field, thus verifying the effectiveness of the method. © 2021, Power System Technology Press. All right reserved.
引用
收藏
页码：3314 / 3323
页数：9
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共 26 条
  • [1] ZHENG Tao, LIU Xiaoxiao, New principle of protection scheme based on fundamental component of control winding currents against turn-to-turn fault of MCSR, Power System Technology, 43, 8, pp. 3016-3024, (2019)
  • [2] HUANG Xinbo, ZHANG Long, ZHU Yongcan, Et al., Online inter-turn insulation monitoring technology based on power angle characteristics for dry-type air-core reactor, Electric Power Automation Equipment, 39, 2, pp. 143-148, (2019)
  • [3] LIU Chang, SHAO Shanfeng, LIAO Ruijin, Et al., Study on the aging characteristics of insulation oil of UHVDC converter transformer, High Voltage Engineering, 45, 3, pp. 730-736, (2019)
  • [4] HAN Saisai, LIU Baozhu, AI Xin, Et al., Dynamic failure rate model for transformer considering insulation aging and oil chromatographic monitoring data, Power System Technology, 42, 10, pp. 3275-3281, (2018)
  • [5] ZHOU Lijun, TANG Haolong, WANG Lujia, Et al., Modeling and analysis of transformer overload based on top oil temperature rise, High Voltage Engineering, 45, 8, pp. 2502-2508, (2019)
  • [6] ZHANG Jiangong, WANG Yanzhao, CHEN Yuchao, Et al., Measurement for sound power of a UHV transformer based on a combined vibration and sound pressure method, High Voltage Engineering, 45, 6, pp. 1843-1850, (2019)
  • [7] ZHANG Qixue, HUANG Haiyan, CHEN Xiang, Et al., Analysis of SBF reactor inter-turn fault and corresponding protection, Electric Power Automation Equipment, 37, 5, pp. 74-79, (2017)
  • [8] ZHENG Tao, ZHAO Yanjie, Overview of key techniques of EHV/UHV controllable shunt reactor, Automation of Electric Power Systems, 38, 7, pp. 127-135, (2014)
  • [9] YANG Zhuang, ZHOU Qu, ZHAO Yaohong, Et al., Prediction of interfacial tension of transformer oil based on artificial neural network and multi-frequency ultrasonic testing technology, High Voltage Engineering, 45, 10, pp. 3343-3349, (2019)
  • [10] DING Yujian, ZHANG Yuehua, ZHOU Songsong, Et al., Study on electric field distribution and switching impulse discharge characteristics of ±1100kV dry-type smoothing reactor, Power System Technology, 41, 11, pp. 3414-3419, (2017)
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