Open AccessTuning a model predictive controller for doubly fed induction generator employing a constrained genetic algorithm
Author(s) -
Rodrigues Lucas L.,
Potts Alain S.,
Vilcanqui Omar A. C.,
Sguarezi Filho Alfeu J.
Publication year - 2019
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
ISSN - 1751-8679
DOI - 10.1049/iet-epa.2018.5922
Subject(s) - model predictive control , overshoot (microwave communication) , control theory (sociology) , generator (circuit theory) , genetic algorithm , controller (irrigation) , doubly fed electric machine , state space representation , power (physics) , computer science , algorithm , engineering , control (management) , control engineering , mathematics , mathematical optimization , ac power , voltage , physics , electrical engineering , quantum mechanics , artificial intelligence , agronomy , biology , telecommunications
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.