PMSWG Parameter Identification Method Based on Improved Operator Genetic Algorithm

Abstract

Permanent Magnet Synchronous Wind Generator (PMSWG) parameter identification method with improved operator genetic algorithm is proposed for the influence of perturbations caused by mechanical parameter changes on the dynamic performance of motor speed control system. Firstly, current with i d = 0 and i d ̸ = 0 are injected into axis d respectively to design the fitness function. Through quantum coding, the genetic algorithm can obtain better population and fitness in the early stage, and find better solutions in the search space. At the same time, the cross method of two random numbers is used to make the cross variable not restricted in a range, which enhances the global search ability. Finally, the update strategy of hybrid mutation composed of Gaussian mutation and Cauchy mutation is introduced to ensure the global search ability of the algorithm, and the accuracy of the optimization results is improved. Experiments show that the proposed method avoids local optimization and achieves global optimization, which can further improve the convergence speed and identification accuracy of the algorithm.