ENHANCING WIND FARM PERFORMANCE THROUGH A PARTICLE SWARM-BASED OPTIMIZATION OF TURBINE DIMENSIONS AND LAYOUT

Wind energy plays a crucial role in the global transition toward sustainable power generation. To meet the growing demand for renewable energy, optimizing wind farm design is essential for both maximizing energy output and minimizing costs. This paper builds upon previous studies by refining and expanding several ideal wind turbine configurations, using them as the foundation for a more comprehensive analysis and enhancement. While prior research has primarily focused on reducing the cost per kilowatt (cost/kW) of power generated, this study takes a broader approach, aiming to optimize overall wind farm efficiency through the strategic adjustment of turbine rotor diameters and hub heights. By implementing a particle swarm optimization (PSO) algorithm, this work identifies the optimal arrangement of these turbine dimensions to achieve higher efficiency while simultaneously reducing overall costs. The proposed methodology offers a flexible, scalable solution that can significantly enhance wind farm performance, making it more adaptable to varying environmental conditions and economic constraints.