Genetic Algorithm Tuned Controllers for High-Performance Indirect Field-Oriented Control in DFIG-Based WECS

Samira Heroual; Belkacem Belabbas; Kheloud Ayati; Rabia Haloui; Ahmed Tawfik Hassan; Alfian Ma’arif; Mohamed Metwally Mahmoud; Vojtech Blazek

doi:10.12928/biste.v8i1.15529

Authors

Samira Heroual University of Tiaret
Belkacem Belabbas University of Tiaret
Kheloud Ayati University of Tiaret
Rabia Haloui University of Tiaret
Ahmed Tawfik Hassan Aswan University
Alfian Ma’arif Universitas Ahmad Dahlan
Mohamed Metwally Mahmoud Aswan University
Vojtech Blazek Technical University of Ostrava

DOI:

https://doi.org/10.12928/biste.v8i1.15529

Keywords:

Wind Turbine, Doubly-Fed Induction Generator, Indirect Field-Oriented Power Control, Classical Proportional-Integral, Genetic Algorithm

Abstract

Due to rising environmental awareness, rising fuel prices, and increasing power consumption, wind power is currently the world's fastest-growing electricity source. One essential form of renewable energy generation is wind energy conversion using a Doubly Fed Induction Generator (DFIG). Moreover, DFIGs are the best option, as wind turbines with variable speeds often have substantial megawatt capacity. Their cost-effectiveness, high operational efficiency, adaptable control mechanisms, and capacity to autonomously regulate the exchange of active and reactive power are the reasons for this selection. Classical control, which is based on PI regulators and employs several loops, is the most popular control approach that makes use of the indirect field-oriented vector method. In order to ensure stability across the whole speed range, it also requires strict regulation and is highly dependent on the correctness of the machine parameters. This paper presents a comparison between the classical PI and the metaheuristic Genetic Algorithm (GA), aiming to enhance the power extraction of DFIG under varying wind conditions. The simulation was carried out using MATLAB-SIMULINK, enabling the exploration of its performance across a range of operational scenarios. The results indicate that the PI controller optimized by GA demonstrates significant improvements over traditional controllers, particularly noted for its simplicity, faster convergence, and greater efficiency in power management.

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