Real-time estimation and tracking of parameters in permanent magnet synchronous motor using a modified two-stage particle swarm optimization algorithm

Reliable controller design for permanent magnet synchronous motor requires knowledge of the motor parameters to build an accurate model of the system. In real-time operation, various environmental and internal factors affect the parameters value which consequently influence the performance of the controller over time. The former studies used the extracted data in off-line mode of operation to estimate the parameters. In this study, the particle swarm optimization algorithm is implemented to estimate the parameters of PMSM (stator resistance, inductances, and the rotor permanent magnet (PM) flux linkage) during motor operation. The effects of the temperature rise on PMSM parameters are investigated and the stator resistance change is successfully tracked with the proposed Two-Stage Single-Flock Particle Swarm Optimization method in steady-state operation condition. The simulation is based on a proper model of the PMSM, including electromagnetic and mechanical elements. The parameters are calculated and minimized via a normalized root mean square error.