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

Author(s)

Rodrigues, Lucas L. ^[1] ; Potts, Alain S. ^[1] ; Vilcanqui, Omar A. C. ^[2] ; Sguarezi Filho, Alfeu J. ^[1]

Affiliation

^[1] Fed Univ ABC UFABC, Santo Andre, SP - Brazil

^[2] Fed Univ Acre UFAC, Rio Branco, AC - Brazil

• Document type: Journal article
• Source: IET ELECTRIC POWER APPLICATIONS; v. 13, n. 6, p. 819-826, JUN 2019.
• Web of Science Citations: 0

Abstract

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. (AU)

• FAPESP's process: 17/04623-3 - Predictive control applied to the input converter for photovoltaic systems
• Grantee: Alfeu Joãozinho Sguarezi Filho
• Support Opportunities: Regular Research Grants
• FAPESP's process: 16/08645-9 - Interdisciplinary research activities in electric smart grids
• Grantee: João Bosco Ribeiro do Val
• Support Opportunities: Research Projects - Thematic Grants