Meta-heuristic Based Optimization Techniques for the Steady-state Analysis of Self-excited Induction Generator

Abstract

The self-excited induction generator is found to be the most suitable generator in wind turbine applications for the extraction of electrical energy from the moving wind. This machine has the advantage of generating electrical energy from the variable wind speed. This paper aims to the use of different optimization algorithms for the analysis of the steady-state performance of the Self-excited induction generator (SEIG) feeding a resistive load. The nodal admittance method is used to build up the equivalent circuit equations of the SEIG which are highly non-linear in nature. These nonlinear equations are solved by the three optimization techniques such as Teaching-Iearning-based optimization (TLBO), Genetic algorithm (GA), and Particle Swarm Optimization (PSO). The generated frequency and the magnetization reactance of the machine are found out and also the obtained simulated results are compared with the experimental data.