Dynamic state estimation for a synchronous generator based on the Koopman operator and Kalman filter

被引：0

作者：

Jiao P. ^{[1
]}

Yang D. ^{[2
]}

Cai G. ^{[1
]}

机构：

[1] School of Electrical Engineering, Northeast Electric Power University, Jilin

[2] School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2024年 / 52卷 / 09期

关键词：

data-driven; dynamic state estimation; Hankel dynamic mode decomposition; Kalman filter; Koopman operator; model;

D O I：

10.19783/j.cnki.pspc.231088

中图分类号：

学科分类号：

摘要：

Dynamic state estimation is an important means of monitoring the dynamic behavior of synchronous generators, and accurate results are important for guiding safe operation and efficient control of power systems. From a data-driven perspective, this paper proposes a method for estimating the dynamic state of synchronous generators based on the Koopman operator and Kalman filter. The method first extracts the Koopman operator from synchronous generator dynamic response data using the Hankel dynamic mode decomposition algorithm, and then constructs a state space model of the synchronous generators based on the extracted Koopman operator. The state variables of synchronous generators are dynamically estimated by Kalman filter. The algorithm does not require prior construction of generator models or parameters and achieves fully data-driven dynamic state estimation. Simulation results show that this algorithm has good adaptability and robustness and exhibits significantly higher accuracy than traditional model-based estimation results using mismatched generator models and parameters. © 2024 Power System Protection and Control Press. All rights reserved.

引用

页码：27 / 35

页数：8

共 29 条

[1]

QI Jinshan, YAO Liangzhong, LIAO Siyang, Et al., Online probabilistic assessment of static voltage stability margin for power systems with a high proportion of renewable energy, Power System Protection and Control, 51, 5, pp. 47-57, (2023)

[2]

WANG Zhong, LI Lili, LI Zhenhua, Et al., The evolution characteristics of power grid frequency probability distribution, Power System Protection and Control, 49, 20, pp. 65-73, (2021)

[3]

MA Ye, SHI Liping, LI Heng, Et al., Power frequency oscillation of a microgrid inverter based on VSG control, Power System Protection and Control, 50, 1, pp. 107-115, (2022)

[4]

SU Peng, CHEN Lu, WU Jian, Et al., Dynamic optimal control model of multi-energy inertia in new power system, Electric Power Construction, 43, 9, pp. 87-93, (2022)

[5]

SONG Yulu, FAN Yanfang, LIU Muyang, Et al., State estimation method of a new energy power system based on SC-DNN and multi-source data fusion, Power System Protection and Control, 51, 9, pp. 177-187, (2023)

[6]

WEI Xinchi, QIN Longyu, FANG Chen, Et al., Review of the synchronous measurement technology and its application in distribution networks, Electric Power Construction, 44, 5, pp. 13-22, (2023)

[7]

CAO Peng, LIU Min, HANG Luqing, Research on PMU optimal configuration of distribution networks based on improved krill herd algorithm, Power System and Clean Energy, 38, 4, pp. 61-67, (2022)

[8]

JU Yuntao, HUANG Yan, State estimation for an AC/DC hybrid power system adapted to non-smooth characteristics, Power System Protection and Control, 51, 2, pp. 141-150, (2023)

[9]

FU Yang, ZHANG Yuhan, TIAN Shuxin, Et al., Active distribution network state estimation method against multi-point false data injection attacks, Smart Power, 51, 4, pp. 69-76, (2023)

[10]

TIAN Junxiang, CHEN Tie, CHEN Bin, Robust dynamic state estimation method for medium voltage distribution networks based on improved adaptive UKF algorithm, Electric Power, 56, 11, pp. 128-133, (2023)

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