Identification of Synchronous Motor Parameters Using Multi-link Improvement Harris Hawks Optimization

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, this paper proposes a Multi-link improvement Harris Hawks Optimization (MIHHO) .Firstly, Logistic chaotic mapping is introduced from the initialization direction of the population to initialize the position of the eagle group, increase the diversity of the population, and accelerate the convergence speed of the algorithm in the early stage. Secondly, from the perspective of the position update of the eagle, the random reverse learning strategy is used to optimize the worst position individual in the eagle, so as to improve the fuzziness and randomness of the algorithm, enhance the global search performance and accelerate the convergence of the algorithm. Finally, in order to prevent premature convergence, the best individuals of this iteration are retained to enter the next iteration to improve the problem that the traditional intelligent algorithm is prone to local optimization and precision decline. On the basis of the mathematical model based on the PMSM voltage equation, the PMSM parameter identification model is built on the simulation platform to test the MIHHO and the standard Harris Hawks Optimization (HHO), particle swarm optimization (PSO) and Sparrow search algorithm (SSA).The simulation results show that MIHHO has better stability, convergence speed and higher identification accuracy for PMSM parameter identification.