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https://doi.org/10.37798/2026751739

Smart Hybrid Metaheuristic Model for Enhanced Wind Energy Production

Muhammad Rashid ; Department of Electrical Engineering, Faculty of Engineering & Technology, The Islamia University of Bahawalpur *
Syed Mohammad Ali Shah ; Hyderabad Institute for Technology and Management Sciences
Abdur Raheem ; Department of Electrical Engineering, Faculty of Engineering & Technology, The Islamia University of Bahawalpur
Saeed Uddin Shaikh ; Hyderabad Institute for Technology and Management Sciences
Rabia Shakoor ; Department of Electrical Engineering, Faculty of Engineering & Technology, The Islamia University of Bahawalpur
Zeeshan Ahmad Arfeenare ; Department of Electrical Engineering, Faculty of Engineering & Technology, The Islamia University of Bahawalpur

* Dopisni autor.

Sažetak

This study presents a hybrid Particle Swarm Optimization–Genetic Algorithm (PSO-GA) technology integrated into a structured three-phase strategy to address the wind farm layout optimization (WFLO) problem. In order to enhance total energy efficiency through intelligent turbine location, the proposed strategy is applied to a particular wind farm scenario. Three case studies, each representing varying degrees of wake and non-wake settings, are analyzed to assess the robustness of this method. In order to prevent severe wake interference, the system finds the best location for turbines while strictly following to industry-standard spacing standards. The suggested hybrid model consistently improves energy extraction and reduces wake losses by 20–28% in all scenarios when compared to current method like PSO-based design by [21]. The hybrid PSO-GA still has a moderate computational cost, taking about twenty seconds each simulation. This is just 10–15% more than standalone PSO, but it produces far greater convergence stability.

Ključne riječi

Hybrid PSO-GA Algorithm; Wind Farm Layout Optimization (WFLO); Wind Turbine Placement

Hrčak ID:

345720

URI

https://hrcak.srce.hr/345720

Datum izdavanja:

14.1.2026.

Posjeta: 113 *