Multi-parameter Identification of Permanent Magnet Synchronous Motor with Improved Sea-horse Optimization Algorithm
To address the slow speed and lack of accuracy in the parameter identification of permanent magnet synchronous mo-tor(PMSM),a sea-horse optimization algorithm integrating cloud model and chaotic variation was proposed.Based on the sea-horse optimization algorithm,the algorithm introduced chaotic mapping and stochastic inverse learning strategy to improve the initial spa-tial distribution of the population.The adaptive cloud model was adopted to solve the low convergence accuracy of the algorithm and reduce the falling into the local optimum;Chaotic mapping and Gaussian variation were incorporated to regulate the distribution of the population in order to improve the algorithm's global and local development capability.By collecting the information of motor voltage and angular velocity,using the improved algorithm to identify the motor parameters in the PMSM identification model.From the simulation and experimental comparison,it is verified that the improved algorithm has a faster,more stable,and accurate recogni-tion effect in the recognition of electrical and mechanical parameters of PMSM,and the recognition errors are all within 1.4％.
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