Application Of Metaheuristic Algorithms For Steady State Analysis Of Six-Phase Self-Exited Induction Generator

Over the past few decades, there has been an increasing use of induction generator particularly in wind power applications. Induction generator needs reactive power for its excitation. In the case of stand alone operation this power can be provided by a capacitor bank connected to its terminals. In this mode of operation, the performances of the induction generator are unknown and have to be computed for a given speed, excitation capacitance and load impedance. Hence, steady-state analysis is needed for a better understanding of the operational behavior of the self-excited induction generator. This paper presents the application of genetic algorithm (GA) and particle swarm optimization (PSO) technique to analyse the six-phase self-excited induction generator (SP-SEIG) performances. A study of some simulation results is carried out using MATLAB to compare between these two algorithms.