Tuning Parameters of Genetic Algorithms for Wind Farm Optimization Using the Design of Experiments Method

Wahiba El Mestari, Nawal Cheggaga, Feriel Adli, Abdellah Benallal and Adrian Ilinca ()
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Wahiba El Mestari: Laboratory of Electrical Systems and Remote Control, Université de Blida 1, Blida 09000, Algeria
Nawal Cheggaga: Laboratory of Electrical Systems and Remote Control, Université de Blida 1, Blida 09000, Algeria
Feriel Adli: Theoretical Physics and Radiation-Matter Interactions Laboratory, Université de Blida 1, Blida 09000, Algeria
Abdellah Benallal: Department of Engineering, Université du Québec à Rimouski, Rimouski, QC G5L 3A1, Canada
Adrian Ilinca: Mechanical Engineering Department, Ecole de Technologie Supérieure, Montreal, QC H3C 1K3, Canada

Sustainability, 2025, vol. 17, issue 7, 1-20

Abstract: Wind energy is a vital renewable resource with substantial economic and environmental benefits, yet its spatial variability poses significant optimization challenges. This study advances wind farm layout optimization by employing a systematic genetic algorithm (GA) tuning approach using the design of experiments (DOE) method. Specifically, a full factorial 2 2 DOE was utilized to optimize crossover and mutation coefficients, enhancing convergence speed and overall algorithm performance. The methodology was applied to a hypothetical wind farm with unidirectional wind flow and spatial constraints, using a fitness function that incorporates wake effects and maximizes energy production. The results demonstrated a 4.50% increase in power generation and a 4.87% improvement in fitness value compared to prior studies. Additionally, the optimized GA parameters enabled the placement of additional turbines, enhancing site utilization while maintaining cost-effectiveness. ANOVA and response surface analysis confirmed the significant interaction effects between GA parameters, highlighting the importance of systematic tuning over conventional trial-and-error approaches. This study establishes a foundation for real-world applications, including smart grid integration and adaptive renewable energy systems, by providing a robust, data-driven framework for wind farm optimization. The findings reinforce the crucial role of systematic parameter tuning in improving wind farm efficiency, energy output, and economic feasibility.

Keywords: wind farm optimization; genetic algorithms; designs of experiments; wake effect; full factorial design; parameter tuning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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