Enhanced fireworks algorithm to optimize extended Kalman filter speed estimation of an induction motor drive system

The Extended Kalman Filter (EFF) is often used for speed estimation in sensorless control of induction motors (IM). The major issue with implementation is the selection of the process of calculating system and noise covariance matrices; traditionally determined using trial-and-error "tuning" or genetic algorithm optimization. In this paper, we study the Enhanced Fireworks Algorithm (EFWA) as a method of optimizing the Extended Kalman Filter parameters for estimating the rotor speed. The EFWA algorithm explores and exploits the search space for the optimal solution by using cooperative "swarm" intelligence. A Simulink model of a system comprised of an induction motor controlled by a variable frequency drive (VFD) operating in constant volts/hertz (V/Hz) mode is used to experiment with the method under varying operating conditions. Our results indicate that EFWA optimization provides better results than the alternative state-of-the-art genetic algorithm (GA) for a comparable number of parameter set trials.