Thermal exchange optimization based control of a doubly fed induction generator in wind energy conversion systems

This paper presents a novel design method to attain the optimal parameters of Proportional-Integral (PI) controllers for a 1.5 MW Doubly Fed Induction Generator (DFIG) in wind energy based on a Thermal Exchange Optimization (TEO) algorithm. Since the gains of PI controllers are usually selected by classical and tedious trials-errors based procedures, their tuning for such a wind energy converter is formulated as a constrained nonlinear optimization problem. Inspired from the Newton’s law of cooling, the TEO algorithm is successfully applied to solve such a control problem under time-domain performances and operational constraints in order to extract the maximum available power. In order to evaluate the performances of the proposed TEO algorithm, the genetic algorithm, particle swarm optimization, harmony search algorithm and water cycle algorithm methods have been utilized for making a comparative study. Moreover, a statistical analysis using non-parametric Friedman and Bonferroni-Dunn’s tests demonstrates that the TEO algorithm gives very competitive results in comparison to the other reported metaheuristic algorithms.