Dynamic state estimation of a grid-connected converter of a renewable generation system using adaptive cubature Kalman filtering. (December 2022) - Zhang, Jing; Bi, Tianshu; Liu, Hao
Highlights: A novel dynamic state estimation (DSE) method for the grid-connected converter of a renewable energy generation system using an adaptive cubature Kalman filter (ACKF) is proposed. A new local deployment mode and sampling method of the DSE program is proposed to achieve high accuracy in real time. A mathematical model of the grid-connected converter was established based on the proposed reasonable modeling assumptions. The DSE is implemented using the proposed ACKF algorithm, which is an improvement of the CKF combined with the Sage-Husa adaptive filter. Simulation results verify the feasibility and estimation accuracy of the proposed DSE method. Abstract: Renewable energy sources are typically integrated with the grid through power electronic converters. A novel dynamic state estimation (DSE) method for the grid-connected converter of a renewable energy generation system using an adaptive cubature Kalman filter (ACKF) is proposed. Different from the traditional SE, the DSE program is deployed locally in the converter, and the voltage and current sampling values of the point of common coupling (PCC) and DC bus rather than the phasors are used for estimation to achieve high accuracy in real time. Based on reasonable modeling assumptions proposed, a mathematical model of the grid-connected converter is established using the most typical topology and control strategy. Further, the DSE is implemented using the proposed ACKF algorithm, which is an improvement of the CKF combined with the Sage-Husa adaptive filter to enable on-line iterative revision of the posterior statistics of the process noise while performing recursive filtering. Thus, the ACKF has stronger adaptability and higher estimation accuracy than the CKF. The simulation results verify the feasibility and estimation accuracy of the proposed DSE method for converters.