Load parameter identification method of power system with time delay based on Kalman filter

被引:0
|
作者
Wang S. [1 ]
He S. [2 ]
机构
[1] Smart Manufacturing College, Zhengzhou University of Economics and Business, Zhengzhou
[2] Intelligent Manufacturing and Mechanical Engineering, Hunan Institute of Technology, Hengyang
关键词
equivalent circuit; excitation control; Kalman filter; load parameter identification; power function; time delay power system;
D O I
10.1504/IJETP.2023.134162
中图分类号
学科分类号
摘要
Aiming at the problems of low accuracy and large identification error of existing load parameter identification methods, a load parameter identification method of power system with time delay based on Kalman filter is proposed. Firstly, according to the relationship between the time-delay link and the voltage variation in the system, the operation characteristics of the time-delay power system are analysed. Secondly, power function and motor equivalent circuit method are used to characterise different property parameters, and the property analysis of load parameters of power system with time delay is completed. Finally, the load parameter state prediction equation is constructed, and the Kalman gain value of the load parameter is calculated. The parameter identification model of Kalman filter is constructed to complete the power system load parameter identification. The experimental results show that the proposed method can reduce the error of load parameter identification, and the minimum error is only 0.11%. Copyright © 2023 Inderscience Enterprises Ltd.
引用
收藏
页码:208 / 219
页数:11
相关论文
共 50 条
  • [31] Short-term power load forecasting of GWO-KELM based on Kalman filter
    Chen, Xiaoyu
    Wang, Yulin
    Tuo, Jianyong
    IFAC PAPERSONLINE, 2020, 53 (02): : 12086 - 12090
  • [32] Estimation of buoy drifting based on adaptive parameter-varying time scale Kalman filter
    Xue, Han
    Chai, Tian
    JOURNAL OF CONTROL AND DECISION, 2021, 8 (03) : 353 - 362
  • [33] An identification system design using a CMAC with a learning algorithm based on the Kalman filter
    Hirashima, Y
    Iiguni, Y
    (SYSID'97): SYSTEM IDENTIFICATION, VOLS 1-3, 1998, : 1333 - 1338
  • [34] Variance Based Analysis for an Isolated Power System Using Kalman Filter and LQR
    Pillai, Arm G.
    Samuel, Elizabeth Rita
    Unnikrishnan, A.
    IEEE INDICON: 15TH IEEE INDIA COUNCIL INTERNATIONAL CONFERENCE, 2018,
  • [35] GB-InSAR Time Series Processing Method Based on Kalman Filter
    Yang H.
    Du J.
    Liu Y.
    Han J.
    Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 2023, 43 (11): : 1105 - 1114
  • [36] Kalman Filter-Based Control System for Power Quality Conditioning Devices
    Kanieski, Joao Marcos
    Cardoso, Rafael
    Pinheiro, Humberto
    Gruendling, Hilton Abilio
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2013, 60 (11) : 5214 - 5227
  • [37] A Robust Deadzone Compensation Method Against Parameter Variations based on Kalman Filter and Neural Networks
    Pei, Le
    Li, Liyi
    Liu, Jiaxi
    Cheng, Zhenxing
    Guo, Qingbo
    Liu, Hongchen
    IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2021,
  • [38] A Real-Time Traffic Detection Method Based on Improved Kalman Filter
    Li Xun
    Nan Kaikai
    Liu Yao
    Zuo Tao
    2018 3RD INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION ENGINEERING (ICRAE), 2018, : 122 - 126
  • [39] Comparison Between WLS and Kalman Filter Method for Power System Static State Estimation
    Hernandez, Carlos
    Maya-Ortiz, Paul
    2015 INTERNATIONAL SYMPOSIUM ON SMART ELECTRIC DISTRIBUTION SYSTEMS AND TECHNOLOGIES (EDST), 2015, : 47 - 52
  • [40] An in-time damage identification approach based on the Kalman filter and energy equilibrium theory
    Huang, Xing-huai
    Dyke, Shirley
    Xu, Zhao-dong
    JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A, 2015, 16 (02): : 105 - 116