El Mestari, Wahiba, Cheggaga, Nawal, Adli, Feriel, Benallal, Abdellah et Ilinca, Adrian.
2025.
« Tuning parameters of genetic algorithms for wind farm optimization using the design of experiments method ».
Sustainability, vol. 17, nº 7.
Compte des citations dans Scopus : 2.
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Résumé
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 22 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.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Ilinca, Adrian |
| Affiliation: | Génie mécanique |
| Date de dépôt: | 30 avr. 2025 16:11 |
| Dernière modification: | 02 mai 2025 18:54 |
| URI: | https://espace2.etsmtl.ca/id/eprint/30859 |
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