Adaptive Dynamic State Estimation of Distribution Network Based on Interacting Multiple Model

被引:38
作者
Kong, Xiangyu [1 ]
Zhang, Xiaopeng [2 ]
Zhang, Xuanyong [1 ]
Wang, Chengshan [1 ]
Chiang, Hsiao-Dong [3 ]
Li, Peng [4 ]
机构
[1] Tianjin Univ, Minist Educ, Key Lab Smart Grid, Tianjin 300072, Peoples R China
[2] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
[3] Cornell Univ, Sch Elect & Comp Engn, Ithaca, NY 14853 USA
[4] Tianjin Univ, Minist Educ, Lab Smart Grid, Tianjin 300072, Peoples R China
关键词
Distribution networks; Adaptation models; State estimation; Estimation; Load modeling; Covariance matrices; Adaptive systems; Dynamic state estimation; extended Kalman filter; interacting multiple models; unscented Kalman filter; SYSTEM; MARKET;
D O I
10.1109/TSTE.2021.3118030
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
With the large-scale access of all kinds of distributed generations (DGs), the operation mode of the distribution network is increasingly diverse and changeable. To monitor the operation of an active distribution network, an adaptive dynamic estimation method is proposed to address the new generation of power system. Considering the features of different types of operation scenario change of distribution network and DGs, the proposed method uses the state deviation index to identify the current operation mode before state estimation. In the adaptive estimation stage, two typical estimators are improved to cope with the typical operation mode and embedded in the interactive multiple model (IMM) algorithm framework. IMM uses the identification results of operation mode to give higher weight to the corresponding estimator and finally outputs the joint estimation results. The proposed estimation method is investigated in an improved IEEE 33-bus system and an actual distribution network in China, which results indicate the proposed method converges more quickly and maintains better accuracy while facing the complex distribution network.
引用
收藏
页码:643 / 652
页数:10
相关论文
共 50 条
  • [21] Dynamic state estimation for power system based on an adaptive unscented Kalman filter
    Zhao, H. (zhaohshcn@126.com), 1600, Power System Technology Press (38): : 188 - 192
  • [22] Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter
    Wenkang, Wan
    Jingan, Feng
    Bao, Song
    Xinxin, Li
    APPLIED SCIENCES-BASEL, 2021, 11 (22):
  • [23] Research on Adaptive Synthesis Dynamic Load Model Based on Multiple Model Ideology
    Ma, Yangyang
    Wang, Zhenshu
    Jiang, Xiaohui
    Fan, Bowen
    2015 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2015,
  • [24] Similarity-Based Multiple Model Adaptive Estimation
    Assa, Akbar
    Plataniotis, Konstantinos N.
    IEEE ACCESS, 2018, 6 : 36632 - 36644
  • [25] Design of State Estimation Based Model Predictive Controller for a Two Tank Interacting System
    Geetha, M.
    Jerome, Jovitha
    Devatha, V
    INTERNATIONAL CONFERENCE ON DESIGN AND MANUFACTURING (ICONDM2013), 2013, 64 : 244 - 253
  • [26] Dynamic state estimation of distribution network under Markov DOS attack
    Wang, Yihe
    Zhang, Na
    Yang, Fangyuan
    Yang, Shuo
    Yang, Bo
    Wang, Huan
    ELECTRONICS LETTERS, 2024, 60 (18)
  • [27] Dynamic state estimation method of distribution network based on partition of AMI total measurement points
    Wang Y.
    Xing A.
    Qu Z.
    Xin S.
    Guo K.
    Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2023, 43 (07): : 142 - 150
  • [28] Koopman Kalman Particle Filter for Dynamic State Estimation of Distribution System
    Wang, Kai
    Liu, Min
    He, Wang
    Zuo, Chaowen
    Wang, Fanyun
    IEEE ACCESS, 2022, 10 : 111688 - 111703
  • [29] Adaptive Kernel Density Theory Based State Estimation for Hybrid AC/DC Distribution Network with PET
    Jia H.
    Si J.
    Mu Y.
    Liu Y.
    Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology, 2021, 54 (07): : 754 - 762
  • [30] Dynamic electrical impedance imaging with the interacting multiple model scheme
    Kim, KY
    Kim, BS
    Kim, MC
    Kim, S
    Isaacson, D
    Newell, JC
    PHYSIOLOGICAL MEASUREMENT, 2005, 26 (02) : S217 - S233