电力系统保护与控制2024，Vol.52Issue(9)：27-35,9.DOI:10.19783/j.cnki.pspc.231088
基于Koopman算子与卡尔曼滤波的同步发电机动态状态估计
Dynamic state estimation for a synchronous generator based on the Koopman operator and Kalman filter
摘要
Abstract
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.关键词
动态状态估计/模型/数据驱动/Koopman算子/卡尔曼滤波/汉克尔动态模态分解Key words
dynamic state estimation/model/data-driven/Koopman operator/Kalman filter/Hankel dynamic mode decomposition引用本文复制引用
焦鹏悦,杨德友,蔡国伟..基于Koopman算子与卡尔曼滤波的同步发电机动态状态估计[J].电力系统保护与控制,2024,52(9):27-35,9.基金项目
This work is supported by the Science and Technology Project of the Headquarters of State Grid Corporation of China(No.5108-202299255A-1-0-ZB). 国家电网有限公司总部科技项目资助(5108-202299255A-1-0-ZB) （No.5108-202299255A-1-0-ZB）
国家重点研发计划项目资助(2021YFB 2400800) （2021YFB 2400800）
