Parameter identification of permanent magnet synchronous motor based on improved particle swarm optimization

In recent years, with the rapid development of the new energy industry, the development of permanent magnet synchronous motors(PMSM) has also ushered in a new stage. For PMSM, many controller designs are based on their mathematical models and use parameters given in the manual. However, in the actual operation process, the parameters of PMSM will change with the environment, resulting in parameter mismatch. In order to solve the problem of parameter variation of PMSM, this paper analyzes the influencing factors. On this basis, aiming at the problems of low precision, slow speed and easy local optimization of standard particle swarm optimization(PSO) algorithm, a cloud adaptive quasi-opposition-based chaotic particle swarm optimization(CAQOCPSO) algorithm is proposed, and simulation is built to verify it. Experimental results show that the proposed algorithm can identify motor parameters accurately, and it is more accurate and faster than the traditional method.