基于改进粒子群算法的双馈感应发电机参数辨识

基本粒子群算法(PSO)存在早熟问题，且惯性权重对参数辨识结果的影响较大，为此提出将变权重PSO算法和全局最优位置变异PSO算法相结合的改进PSO算法，并将其应用于双馈感应发电机(DFIG)的参数辨识。分析了DFIG中各参数的可辨识性和辨识难易度，给出了基于改进PSO算法的参数辨识步骤。与采用基本PSO算法、变权重PSO算法和全局最优位置变异PSO算法的参数辨识结果相比较，该方法具有收敛速度快、辨识误差小的优点，即使在较大的搜索范围内仍具有较高的辨识精度。

Identification of DFIG parameters based on improved PSO algorithm

To overcome the inherent deficiencies in the particle swarm optimization(PSO)algorithm, such as premature convergence, and to take into account the effects of inertia weight on the identification accuracy, an improved PSO algorithm, which combines the adaptive inertia weight PSO algorithm with the global optimum location mutation PSO algorithm, is proposed in this paper, in order to identify the double-fed induction generator(DFIG)parameters. First, the identifiability of the DFIG parameters and the difficulties in identification are analyzed. Then, the identification steps based on this improved PSO algorithm are illustrated. Compared with the basic PSO algorithm, the adaptive inertia weight PSO algorithm, and the global optimum location mutation PSO algorithm, the proposed algorithm has faster convergence, smaller errors, and higher identification accuracy, even at a wide search range.