A New Power System State Estimation Based on Synchronous Generator Dynamic State Estimation

被引：0
作者：
Zhang, Jing [1 ]
Bi, Tianshu [1 ]
Liu, Hao [1 ]
机构：
[1] State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, North China Electric Power University, Changping District, Beijing
来源：
Dianwang Jishu/Power System Technology | 2024年 / 48卷 / 10期
基金：
中国国家自然科学基金;
关键词：
electro-mechanical transient; Kalman filtering; PMU; power system state estimation; synchronous generator dynamic state estimation;
D O I：
10.13335/j.1000-3673.pst.2023.1731
中图分类号：
学科分类号：
摘要：
When a power system suffers from some big disturbance, its security and stability will be threatened. To completely track the electro-mechanical transient process after disturbance, a new power system state estimation (SE-PS) based on dynamic state estimation of synchronous generator (DSE-SG) is proposed, and how to further apply the results of DSE-SG to system-side SE-PS is studied to achieve the unified estimation of the dynamic and static state variables of the entire system. Firstly, focusing on the solution of electro-mechanical transient DSE of the whole power system, the infeasibility of complete synchronization, the realization conditions of decoupling estimation, and the necessity and significance of re-coupling estimation are discussed. Secondly, based on the concepts and mathematical models of DSE-SG and SE-PS, the status, roles, relationships, and data flows of the variables and equations involved are clarified, which lays a theoretical foundation for the selection of re-coupling media and the determination of interface mode and forms the realization framework and idea of re-coupling estimation. Furthermore, two different interface modes are proposed, and their specific implementation methods and processes are given in detail. Finally, the proposed method is implemented in an IEEE9-bus system. The simulation results show that the method can track the electro-mechanical transient process of the whole power system well and realize the unified estimation of dynamic and static state variables. Compared with the traditional SE-PS without DSE-SG, the proposed method has higher accuracy and a more remarkable filtering effect. © 2024 Power System Technology Press. All rights reserved.
引用
收藏
页码：4231 / 4241
页数：10
相关论文
共 26 条
  • [1] pp. 19-35
  • [2] ZHAO Junbo, NETTO M, HUANG Zhenyu, Roles of dynamic state estimation in power system modeling，monitoring and operation [J], IEEE Transactions on Power Systems, 36, 3, pp. 2462-2472, (2021)
  • [3] ZHAO Junbo, NETTO M, Power system dynamic state estimation ： motivations ， definitions ，methodologies，and future work[J], IEEE Transactions on Power Systems, 34, 4, pp. 3188-3198, (2019)
  • [4] Bendong TAN, ZHAO Junbo, NETTO M．, A general decentralized dynamic state estimation with synchronous generator magnetic saturation[J], IEEE Transactions on Power Systems, 38, 1, pp. 960-963, (2023)
  • [5] ZHOU Ning, MENG Da, HUANG Zhenyu, Dynamic state estimation of a synchronous machine using PMU data：a comparative study[J], IEEE Transactions on Smart Grid, 6, 1, pp. 450-460, (2015)
  • [6] CHEN Liang, BI Tianshu, LI Jingsong, Dynamic state estimator for synchronous machines based on cubature Kalman filter[J], Proceedings of the CSEE, 34, 16, pp. 2706-2713, (2014)
  • [7] NETTO M, MILI L．, A robust data-driven Koopman Kalman filter for power systems dynamic state estimation[J], IEEE Transactions on Power Systems, 33, 6, pp. 7228-7237, (2018)
  • [8] BI Tianshu, CHEN Liang, XUE Ancheng, Dynamic state estimator for synchronous machines based on robust cubature Kalman filter[J], Transactions of China Electrotechnical Society, 31, 4, pp. 163-169, (2016)
  • [9] ZHAO Junbo, MILI L．, A theoretical framework of robust H-infinity unscented Kalman filter and its application to power system dynamic state estimation[J], IEEE Transactions on Signal Processing, 67, 10, pp. 2734-2746, (2019)
  • [10] ZHU Maolin, LIU Hao, BI Tianshu, Dynamic state estimation method for generators considering bad data in input[J], Automation of Electric Power Systems, 46, 7, pp. 94-103, (2022)
123