Advanced 3D Wind Farm Layout Optimization Framework via Power-Law Perturbation-Based Genetic Algorithm

doi:10.1109/JAS.2025.125351

Advanced 3D Wind Farm Layout Optimization Framework via Power-Law Perturbation-Based Genetic Algorithm

2025-12-02

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DOI：10.1109/JAS.2025.125351

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The modeling and optimization of wind farm layouts can effectively reduce the wake effect between turbine units, thereby enhancing the expected output power and avoiding negative influence. Traditional wind farm optimization often uses idealized wake models, neglecting the influence of wind shear at different elevations, which leads to a lack of precision in estimating wake effects and fails to meet the accuracy and reliability requirements of practical engineering. To address this, we have constructed a three-dimensional 3D wind farm optimization model that incorporates elevation, utilizing a 3D wake model to better reflect real-world conditions. We aim to assess the optimization state of the algorithm and provide strong incentives at the right moments to ensure continuous evolution of the population. To this end, we propose an evolutionary adaptation degree-guided genetic algorithm based on power-law perturbation (PPGA) to adapt multidimensional conditions. We select the offshore wind power project in Nantong, Jiangsu, China, as a study example and compare PPGA with other well-performing algorithms under this practical project. Based on the actual wind condition data, the experimental results demonstrate that PPGA can effectively tackle this complex problem and achieve the best power efficiency.

Keywords:

3D wake model

China's southeastern coast

meta-heuristic

offshore wind farm

power-law perturbation-based genetic algorithm (PPGA)

Advanced 3D Wind Farm Layout Optimization Framework via Power-Law Perturbation-Based Genetic Algorithm

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