Tuning a model predictive controller for doubly fed induction generator employing a constrained genetic algorithm

This study presents a model predictive control (MPC) for a doubly fed induction generator (DFIG) power control using a state-space prediction model. Genetic algorithms (GAs) have demonstrated their potential in finding good solutions for complex problems. However, GA in its original form lacks a mechanism for handling constraints. In this way, a method for tuning the MPC based on a novel constrained GA is proposed. In this way, the method permits a good solution for the weighing matrices with predetermined maximum requirements, such as maximum overshoot, just using the DFIG control simulation. Finally, experimental results are presented to endorse the proposed theory.