基于优化EKF的永磁同步电机转速估计

Abstract

Abstract: An extended Kalman filter(EKF) speed estimation method based on improved Particle Swarm Optimization(IPSO) is proposed to solve the problem that the estimation of system noise matrix and measurement noise matrix are difficult to obtain accurately when EKF is used to estimate the speed in the sensorless speed direct torque control(DTC) system of permanent magnet synchronous motors(PMSM). This method combines the advantages of Particle Swarm Optimization and Genetic Algorithms. Simulation results show that the IPSO Kalman filter can find the optimum solution more quickly when it is applied to the optimization of Kalman filter system noise matrix and measurement noise matrix comparing with genetic algorithms and standard particle swarm optimization.

Key words: PMSM, direct torque control, speed sensorless, extended Kalman filter, improved particle swarm optimization

CLC Number:

TM341

WANG Jian, HUANG Zhi-gong, XU Jin-hai. Speed Estimation of Permanent Magnet Synchronous Motor Based on Optimized EKF[J].Journal of Guangxi Normal University(Natural Science Edition), 2014, 32(4): 11-17.

References

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Speed Estimation of Permanent Magnet Synchronous Motor Based on Optimized EKF

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