PSO parameters optimization for EKF and AKF for IM rotor speed estimation

This paper presents the application of a Metaheuristic optimization algorithm for determining the parameters of a PI controller and the values of the state and measurement noise of Kalman Filter. The particle swarm optimization is a new technique that is used to solve complex problems. It minimizes a cost function under the cooperation of many individuals. Kalman Filter is used here to estimate the stator currents and rotor fluxes of the induction motor. The performances of the extended Kalman Filter and the adaptive Kalman Filter are analyzed. They are applied to estimate stator currents; rotor fluxes and rotor speed of the induction motor, and thus help to overcome the speed sensor, which is expensive and bulky. The extended Kalman Filter requires extending the state vector to rotor speed, which implies to use the linearization of the model. The adaptive Kalman Filter consists of determining the rotor speed adaptation law. The stability of the estimation error is proved using a Lyapunov function.