Research on combined error prediction method of electronic voltage transformer considering multiple features
CSTR:
Author:
Affiliation:

(1.Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China; 2.College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

Clc Number:

TM451

  • Article
  • | |
  • Metrics
  • |
  • Reference [25]
  • | | | |
  • Comments
    Abstract:

    The measurement accuracy of electronic voltage transformer (EVT) is closely related to the security and economy of the power grid. In order to accurately predict the error of EVT during the long?term operation, a combined error prediction method considering multiple features is proposed. This method selects parameters with strong correlation to EVT errors through correlation analysis as feature quantities. It utilizes a fused attention mechanism LSTM model and an SVR model to predict the errors of the transformer separately. The obtained prediction results are then combined to generate the final prediction result. The real?time operational data of a certain substation is simulated and analyzed. The results indicate that the proposed method can effectively predict the error variation information of the EVT over a certain period of time and has certain reference value for the timely prediction of EVT errors in substations and scheduling of measurement performance maintenance.

    Reference
    [1] 宋晓林,刘浩,刘豪,等.500/31/2 kV 0.002级多级励磁标准电压互感器设计与仿真分析[J].电工技术学报,2021,36(9):1967?1975. SONG Xiaolin,LIU Hao,LIU Hao,et al.Simulation analysis and design of?500/31/2 kV 0.002?multistage excitation standard voltage transformer with accuracy class 0.002[J].Transactions of China Electrotechnical Society,2021,36(9):1967?1975.
    [2] 张有锋,郑运鸿,戴冬云,等.温度对电子式电压互感器测量精度的影响研究[J].高压电器,2022,58(6):221?227. ZHANG Youfeng,ZHENG Yunhong,DAI Dongyun,et al.Research on influence of temperature on metering accuracy of electronic voltage transformer[J].High Voltage Apparatus,2022,58(6):221?227.
    [3] 李永森.电容式电压互感器误差特性影响因素研究[D].重庆:重庆大学,2016. LI Yongsen.Study on the influence factors on the error characteristics of the capacitor voltage transformer[D].Chongqing:Chongqing University,2016.
    [4] 李振华,郑严钢,李振兴,等.基于传递熵和小波神经网络的电子式电压互感器误差预测[J].电测与仪表,2021,58(3):146?152. LI Zhenhua,ZHENG Yangang,LI Zhenxing,et al.Error prediction of electronic voltage transformer based on transfer entropy and wavelet neural network[J].Electrical Measurement & Instrumentation,2021,58(3):146?152.
    [5] 海宾,杨姝楠,陈丽雯,等.基于现场信号仿真技术的电流互感器误差测试技术研究[J].电测与仪表,2021,58(2):133?138. HAI Bin,YANG Shunan,CHEN Liwen,et al.Research on current transformer error testing technology based on field signal simulation technology[J].Electrical Measurement & Instrumentation,2021,58(2):133?138.
    [6] 胡琛,张竹,焦洋,等.基于随机矩阵理论的电子式互感器误差状态相关性分析方法[J].电力自动化设备,2018,38(9):45?53. HU Chen,ZHANG Zhu,JIAO Yang,et al.Error state correlation analysis based on random matrix theory for electronic transformer[J].Electric Power Automation Equipment,2018,38(9):45?53.
    [7] 韩海安,张竹,王晖南,等.基于主元分析的电容式电压互感器计量性能在线评估[J].电力自动化设备,2019,39(5):201?206. HAN Haian,ZHANG Zhu,WANG Huinan,et al.Online metering performance evaluation of capacitor voltage transformer based on principal component analysis[J].Electric Power Automation Equipment,2019,39(5):201?206.
    [8] 严英杰,盛戈皞,王辉,等.基于高维随机矩阵大数据分析模型的输变电设备关键性能评估方法[J].中国电机工程学报,2016,36(2):435?445. YAN Yingjie,SHENG Gehao,WANG Hui,et al.The key state assessment method of power transmission equipment using big data analyzing model based on large dimensional random matrix[J].Proceedings of the CSEE,2016,36(2):435?445.
    [9] 罗龙,李两桓,王成阳,等.基于ARIMA?LSTM的绝缘子状态数据挖掘方法[J].电力科学与技术学报,2017,32(4):38?43. LUO long,LI Lianghuan,WANG Chengyang,et al.Insulator status data mining method based on ARIMA?LSTM[J].Journal of Electric Power Science and Technology,2017,32(4):38?43.
    [10] 刘亚珲,赵倩.基于聚类经验模态分解的CNN?LSTM超短期电力负荷预测[J].电网技术,2021,45(11):4444? 4451. LIU Yahui,ZHAO Qian.Ultra?short?term power load forecasting based on cluster empirical mode decomposition of CNN?LSTM[J].Power System Technology,2021,45(11):4444?4451.
    [11] 陈自强.基于LSTM网络的设备健康状况评估与剩余寿命预测方法的研究[D].合肥:中国科学技术大学,2019. CHEN Ziqiang.research on equipment health assessment and remaining userful life prediction method based on LSTM[D].Hefei:University of Science and Technology of China,2019.
    [12] 张运厚,李婉莹,董福贵.基于DE?GWO?SVR的中长期电力需求预测[J].中国电力,2021,54(9):83?88. ZHANG Yunhou,LI Wanying,DONG Fugui,et al.Medium and long?term power demand forecasting based on DE?GWO?SVR[J].Electric Power,2021,54(9):83?88.
    [13] 李梦涵,赵学文,李建琦,等.基于VMD?SVM的小电流接地系统故障选线方法[J].电网与清洁能源,2021,37(8):1?8. LI Menghan,ZHAO Xuewen,LI Jianqi,et al.Fault line selection method for small current grounding system based on VMD?SVM[J].Power System and Clean Energy,2021,37(8):1?8.
    [14] 吴海涛,代尚林,乔中伟,等.基于RBF?SVM智能配变终端的网络安全态势评估[J].电力科学与技术学报,2021,36(5):35?40. WU Haotao,DAI Shanglin,QIAO Zhongwei,et al.Research on network security situation awareness of intelligent distribution transformer terminal unit based on RBF?SVM[J].Journal of Electric Power Science and Technology,2021,36(5):35?40.
    [15] 李卓,叶林,戴斌华,等.基于IDSCNN?AM?LSTM组合神经网络超短期风电功率预测方法[J].高电压技术,2021,48(6):2117?2127. LI Zhuo,YE Lin,DAI Binhua,et al.Ultra?short?term wind power prediction method based on IDSCNN?AM?LSTM combination neural network[J].High Voltage Engineering,2021,48(6):2117?2127.
    [16] 朱凌建,荀子涵,王裕鑫,等.基于CNN?Bi LSTM的短期电力负荷预测[J].电网技术,2021,45(11):4532?4539. ZHU Lingjian,XUN Zihan,WANG Yuxin,et al.Short?term power load forecasting based on CNN?BiLSTM[J].Power System Technology,2021,45(11):4532?4539.
    [17] 胡浩亮,李前,卢树峰,等.电子式互感器误差的两种校验方法对比[J].高电压技术,2011,37(12):3022?3027. HU Haoliang,LI Qian,LU Shufeng,et al.Comparision of two electronic transformer error measuring methods[J].High Voltage Engineering,2011,37(12):3022?3027.
    [18] 唐登平,蔡文嘉,周翔宇,等.基于VMD和样本熵的电磁式电流互感器故障诊断[J].电力科学与技术学报,2021,36(6):144?150. TANG Dengping,CAI Wenjia,ZHOU Xiangyu,et al.Fault diagnosis of current transformer based on VMD and sample entropy[J].Journal of Electric Power Science and Technology,2021,36(6):144?150.
    [19] 张竹.电容式电压互感器计量误差状态评估和预测方法研究[D].武汉:华中科技大学,2018. ZHANG Zhu.Research on condition evaluation and prediction methods for the metering error of capacitor voltage transformer[D].Wuhan:Huazhong University of Science and Technology,2018.
    [20] 刘季昂,刘友波,邱高,等.基于高斯过程回归的电网运行方式快速置信评价[J].电力系统自动化,2022,46(11):181?190. LIU Ji'ang,LIU Youbo,QIU Gao,et al.Fast confidence evaluation of operation mode of power grid based on gaussian process regression[J].Automation of Electric Power Systems,2022,46(11):181?190.
    [21] 刘军,王苗,严清心,等.基于组合赋权和梯形云模型的发电商市场力评价[J].电力科学与技术学报,2021,36(2):58?66. LIU Jun,WANG Miao,YAN Qingxin,et al.Market power evaluation of generators based on combination weighting and trapezoidal cloud model[J].Journal of Electric Power Science and Technology,2021,36(2):58?66.
    [22] 吴少聪.基于混合模型的股票趋势预测方法研究[D].哈尔滨:哈尔滨工业大学,2017. WU Shaocong.Research on methods of stock trends prediction based on hybrid model[D].Harbin:Harbin Institute of Technology,2017.
    [23] 熊一,詹智红,柯方超,等.基于改进BP神经网络的变电站检修运维成本预测[J].电力科学与技术学报,2021,36(4):44?52. XIONG Yi,ZHAN Zhihong,KE Fangchao,et al.Overhaul operation and maintenance cost prediction of substation based on improved BP neural network[J].Journal of Electric Power Science and Technology,2021,36(4):44?52.
    [24] 张帅可,罗萍萍.基于混合分布模型的风电功率超短期预测误差分析[J].电力科学与技术学报,2020,35(5):111?118. ZHANG Shuaike,LUO Pingping,Wind ultra short?time prediction error analysis of wind power based on mixed distribution model[J].Journal of Electric Power Science and Technology,2020,35(5):111?118.
    [25] LI Z H,LI H B,ZHANG Z,et al.An online calibration method for electronic voltage transformers based on IEC 61850?9?2[J].Mapan?Journal of Metrology Society of INDIA,2014,29(2):201497?105.
    Related
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

钟 悦,李振华,兰 芳.考虑多因素的电子式电压互感器误差组合预测方法研究[J].电力科学与技术学报英文版,2023,38(3):188-196. ZHONG Yue, LI Zhenhua, LAN Fang. Research on combined error prediction method of electronic voltage transformer considering multiple features[J]. Journal of Electric Power Science and Technology,2023,38(3):188-196.

Copy
Share
Article Metrics
  • Abstract:286
  • PDF: 739
  • HTML: 0
  • Cited by: 0
History
  • Online: September 19,2023
Article QR Code