Parameter identification of direct‐drive permanent magnet synchronous generator based on EDMPSO‐EKF

Abstract

Abstract Aiming at the problem that extended Kalman filter (EKF) is difficult to determine the appropriate system and measurement noise matrices in parameter identification of direct‐drive permanent magnet synchronous generator (D‐PMSG), a parameter identification method of D‐PMSG based on mean particle swarm optimization with extreme disturbance (EDMPSO)‐EKF is proposed. Firstly, by analysing the principle of EKF, a dual‐thread identification model is established, then the particle swarm optimization (PSO) algorithm is improved to jump out of the local optimum by adding extremum interference and taking average extremum, and a suitable fitness function is designed. Finally, the improved PSO algorithm is applied to the adaptive optimization of EKF system noise matrix and measurement noise matrix, and the system performs the parameter identification after obtaining the optimal noise matrix. The simulation and experiment results show that the proposed method has better convergence speed and can more accurately identify the parameters of stator resistance, inductance and flux linkage, and it has better identification accuracy and generalization ability than the traditional method.