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

Abstract

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. KF can be extended to estimate the rotor speed. This necessitates linearizing IM model around an operating point. Another alternative is to use adaptive KF, which involves determining an adaptation law. A PI controller can then be used to improve the performance of this solution. The covariance's state and measurement noises are necessary to use KF. The trial and error method can be used to determine them, but it is tedious. Alternatively, the particle swarm optimization (PSO) algorithm is used. This is also applied to refine the PI controller parameters. In this work, extended Kalman Filter ( EKF) and adaptive Kalman Filter (AKF) are developed, then, a PSO algorithm is used to determine the appropriate covariance's noises. Simulation results, for both cases, are carried out. In section II, a discrete-time model of IM is developed. The KF, EKF and AKF algorithms are discussed in section III. The PSO algorithm is presented in section IV. Simulation results and comments are given in section V.