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

被引:41
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 31 条
[1]   A Block Version of Orthogonal Rotations for Improving the Accuracy of Hybrid State Estimators [J].
Bez, Daniel Martins ;
Simoes Costa, Antonio ;
Ascari, Larah Bruning ;
Junior, Edson Zanlorensi .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2020, 35 (06) :4432-4444
[2]   State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters [J].
Cadini, F. ;
Sbarufatti, C. ;
Cancelliere, F. ;
Giglio, M. .
APPLIED ENERGY, 2019, 235 :661-672
[3]   Distribution Market-Clearing and Pricing Considering Coordination of DSOs and ISO: An EPEC Approach [J].
Chen, Houhe ;
Fu, Linbo ;
Bai, Linquan ;
Jiang, Tao ;
Xue, Yaosuo ;
Zhang, Rufeng ;
Chowdhury, Badrul ;
Stekli, Joseph ;
Li, Xue .
IEEE TRANSACTIONS ON SMART GRID, 2021, 12 (04) :3150-3162
[4]   A Robust State Estimation Method Based on SOCP for Integrated Electricity-Heat System [J].
Chen, Yanbo ;
Yao, Yuan ;
Zhang, Ying .
IEEE TRANSACTIONS ON SMART GRID, 2021, 12 (01) :810-820
[5]   Dynamic PV Model Parameter Identification by Least-Squares Regression [J].
Di Piazza, Maria Carmela ;
Luna, Massimiliano ;
Vitale, Gianpaolo .
IEEE JOURNAL OF PHOTOVOLTAICS, 2013, 3 (02) :799-806
[6]   Hybrid Measurements-Based Fast State Estimation for Power Distribution System [J].
He, Xi ;
Li, Chang ;
Du, Mingdi ;
Dong, Heng ;
Li, Peng .
IEEE ACCESS, 2021, 9 :21112-21122
[7]   Real Time Optimal Reconfiguration of Multiphase Active Distribution Networks [J].
Jabr, Rabih A. ;
Dzafic, Izudin ;
Huseinagic, Indira .
IEEE TRANSACTIONS ON SMART GRID, 2018, 9 (06) :6829-6839
[8]   Synchrophasor Measurement-based Modal Analysis in Power Grids [J].
Jiang, Tao ;
Bai, Linquan ;
Li, Xue ;
Li, Fangxing .
2019 51ST NORTH AMERICAN POWER SYMPOSIUM (NAPS), 2019,
[9]  
Ju P, 2018, 2018 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONIC ENGINEERING (ICEEE), P166, DOI 10.1109/ICEEE2.2018.8391323
[10]   Impact of Data Quality in Home Energy Management System on Distribution System State Estimation [J].
Kang, Jeong-Won ;
Xie, Le ;
Choi, Dae-Hyun .
IEEE ACCESS, 2018, 6 :11024-11037