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.

Real time observer-based stator fault diagnosis for IM

“…The Trial-and-error method for EKF tuning is commonly used but can be tedious. To overcome this situation, heuristic approaches, such as a Memetic algorithm [22], PSO algorithm [20] or Genetic algorithm [23] were used to automatically optimize both the state and measurement noise matrices. In this work, the cyclic method [18], which is a simple and an efficient technique, is applied to determine the optimal state and measurement noises.…”

“…EKF is developed to estimate state variables and the fault factor by considering the extended state vector given in (11). EKF algorithm applied for the improved IM model (9) and extended to fault factor , is given bellow [20,21], with the assumption that has no dynamic (15).…”

“…is the measurement auto-covariance. The EKF performance is evaluated by the cost function given in (20).…”