Real-time Dynamic State Estimation for Synchronous Machines Based on Robust CKF

被引：0
|
作者：
Liu P. [1 ]
Xiang Z. [2 ]
Jiang Q. [1 ]
Geng G. [1 ]
Sun W. [2 ]
Xiong H. [3 ]
机构：
[1] College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang Province
[2] State Grid Zhejiang Province Electric Power Company Limited, Hangzhou, 330106, Zhejiang Province
[3] Electric Power Research Institute of State Grid Zhejiang Province Electric Power Company, Hangzhou, 310014, Zhejiang Province
来源：
Dianwang Jishu/Power System Technology | 2019年 / 43卷 / 08期
基金：
国家重点研发计划;
关键词：
Bad data identification and correction; Implicit trapezoidal integral; Real-time dynamic state estimation; Robustness;
D O I：
10.13335/j.1000-3673.pst.2019.0247
中图分类号：
学科分类号：
摘要：
Dynamic state estimation for synchronous machines is of great significance for analysis and control of power system. However, the Kalman filter-based dynamic estimation algorithm is computationally intensive and susceptible to be disturbed by bad measurement data. In this paper, a synchronous power generation system model considering excitation and speed governor is established, and an initial value optimization method is proposed. The implicit trapezoidal integral method is used to discretize the electromechanical transient process. According to the characteristics of Cubature Kalman Filter (CKF) algorithm, a Jacobian matrix reuse strategy is put forward to improve efficiency and meet real-time requirements. Through hypothesis test, the input-output bad data is identified, and a correction method is put forward to improve robustness of the algorithm. Finally, case studies in IEEE 9-bus system and an actual unit in East China Power Grid are performed, verifying effectiveness of the proposed algorithm. © 2019, Power System Technology Press. All right reserved.
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收藏
页码：2860 / 2867
页数：7
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