基于MDPSO的永磁直驱风力发电机参数辨识

针对永磁直驱风力发电机的多参数辨识问题以及传统参数辨识方法的收敛精度差、收敛速度慢等问题,提出了引入平均最优位置变量的自适应空间搜索向量的改进粒子群算法(MDPSO),对永磁直驱风力发电机参数辨识。根据永磁直驱风力发电机定子电压电流模型,进行pade近似并降阶处理后进行离散化,建立直驱风力发电机辨识模型;引入自适应空间搜索向量和平均最优位置变量改进粒子群算法;应用提出的MDPSO辨识直驱风力发电机定子绕组的电阻、电感和磁链等参数。算例仿真结果表明,提出的辨识算法具有精度高、计算速度快、稳定性高等特点,从而验证了建立的直驱风力发电机辨识模型及辨识算法的有效性。

Parameter Identification Method of Permanent Magnet Direct Drive Wind Turbine Based on MDPSO

In this paper, the multi-parameter identification problem of permanent magnet direct-drive wind turbine and the convergence accuracy and slow convergence speed of traditional parameter identification method are proposed. The adaptive space search vector particle swarm optimization (MDPSO) algorithm based on average optimal position variable is proposed. Parameter identification of permanent magnet direct drive wind turbine. Firstly, according to the stator voltage and current model of permanent magnet direct drive wind turbine, the pade approximation and reduced order processing are performed to discretize the generator identification model. Then the adaptive space search vector and the average optimal position variable are introduced to improve the particle swarm optimization algorithm. Finally, Through the MDPSO proposed in this paper, the parameters such as the resistance, inductance and flux linkage of the stator winding of the permanent magnet direct-drive wind turbine are identified. The simulation results show that the proposed identification algorithm has the characteristics of high precision, fast calculation speed and high stability, which verifies the validity of the established direct-drive wind turbine identification model and identification algorithm.