Abstract：Aiming at the problems of the traditional methods in the field of parameter identification of permanent magnet synchronous motor (PMSM), it is difficult to identify multiple parameters at the same time and the identification accuracy is not high enough, parameter identification algorithm was proposed, in which the Harris Eagle optimization algorithm is adopted. In order to improve the accuracy and stability of the identification algorithm, this thesis improves the Harris Eagle algorithm from three aspects: First, the position of the eagle colony is initialized by introducing Logistic chaotic mapping from the direction of population initialization, increasing the diversity of the population and speeding up the convergence of the identification algorithm. Secondly, from the perspective of eagle position update, the random reverse learning strategy is used to optimize the worst position individual in the eagle group, so as to improve the fuzziness and randomness of the algorithm, enhance the global search performance, and make the identification results more accurate. Finally, in order to prevent premature convergence, the current optimal individual position is retained into the next iteration to improve the problem that the traditional Harris Eagle algorithm is prone to local optimization and precision decline. On the basis of the mathematical model based on PMSM voltage equation, MIHHO algorithm and standard Harris Eagle algorithm (HHO), particle swarm optimization (PSO) and Sparrow search algorithm (SSA) are tested.The results show that MIHHO algorithm has better stability, convergence speed and higher identification accuracy for PMSM parameter identification.
2024, Vol. 45 
