基于Koopman算子与卡尔曼滤波的同步发电机动态状态估计OA北大核心CSTPCD

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.