Traditional genetic algorithm identification of permanent magnet synchronous wind generator (PMSWG) parameters is easy to fall into local optimum, resulting in low accuracy of parameter identification results and slow convergence, which reduces the accuracy of parameter tuning of proportional-integral (PI) controller. Aiming at this problem, a chaotic mapping simulated annealing genetic algorithm (CMSAGA) for identifying PMSWG parameters is proposed. The traditional genetic algorithm (GA) has the ability of global random search, combined with the probability breakthrough characteristic of the simulated annealing (SA) algorithm, which avoids the parameter identification result falling into the local optimum and finally tends to the global optimum. With the increase of iteration times, the initial population is mapped with tent chaos mapping theory, and the optimal value of the population is disturbed in each iteration to increase the diversity of the population, making the proposed algorithm converge faster and improve the accuracy. Experiments show that the proposed algorithm has good accuracy and convergence speed, PMSWG stator resistance, stator winding d-q axis inductance and permanent magnet flux can be identified.
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