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A data-physics hybrid-driven layout optimization framework for large-scale wind farms
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AI
DOI：10.1016/j.apenergy.2025.125908.png)
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Abstract
En 中文
The global trend of wind energy utilization moves towards building large-scale and remotely-located wind farms, while strategic layout optimization is crucial to improving the power generation of wind farms. However, largescale wind farm layout optimization (WFLO) faces challenges due to complicated calculations involving the high dimensional decision variables and the need to trade-off between wake model accuracy and efficiency. To address these issues, this paper proposes a novel data-physics hybrid-driven framework for layout optimization of largescale wind farms. This framework attempts to integrate physical equations with variable parameters to guide the modeling of wake effects and further facilitate the layout optimization process. Specifically, the physics-informed dual neural network (PIDNN) model is proposed to estimate the wind velocity. This model incorporates a variable thrust coefficient into the Navier-Stokes equations through dual neural networks. Moreover, the gene-targeted differential evolution (GTDE) algorithm is employed to optimize the wind farm layout, which is particularly designed for large-scale optimization problems. Simulation results demonstrate that the proposed PIDNN can estimate wake velocity effectively. Furthermore, the proposed optimization framework outperforms competing methods, achieving the highest power generation.
Keywords:
Wake model
Wind farm layout optimization
Physics-informed dual neural network
Gene targeting differential evolution