Data-driven modeling of wind farm wake flow based on multi-scale feature recognition
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909805894959104 |
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| author | Xu, Dong Li, Zhaobin Yang, Xiaolei Hou, Peng Carmo, Bruno Mao, Xuerui |
| author_facet | Xu, Dong Li, Zhaobin Yang, Xiaolei Hou, Peng Carmo, Bruno Mao, Xuerui |
| contents | Accurate, efficient prediction of wind flow with wake effects is crucial for wind-farm layout and power forecasting. Existing approaches-physical measurements, numerical simulations, physics-based models, and data-driven models-face trade-offs: the first two are time- and resource-intensive; physics-based models can lack accuracy due to limited physics; data-driven methods leverage abundant, high-quality data and are increasingly popular. We propose a rapid, data-driven wake-flow model inspired by video-frame interpolation and the principle of similarity. Field data are transformed into images; multi-scale feature recognition then identifies, matches, and interpolates wake structures using Scale-Invariant Feature Transform (SIFT) and Dynamic Time Warping (DTW) to generate intermediate flow fields. Six representative mini wind-farm cases validate the approach, spanning variations in turbine spacing, turbine size, combined spacing-size variations, different turbine counts, and wind-direction misalignment. Across cases, the method achieves a mean absolute percentage error (MAPE) of 0.68-2.28%. Because it flexibly computes both 2D and 3D wake fields, the method offers substantial computational-efficiency gains over large-eddy simulation (LES) and Meteodyn WT when 2D accuracy suffices for industrial needs. Accordingly, it provides a practical alternative to measurements, high-fidelity simulations, and simplified physics-based models, enabling efficient expansion of wake-flow databases for wind-farm design and power prediction while balancing speed and accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21005 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven modeling of wind farm wake flow based on multi-scale feature recognition Xu, Dong Li, Zhaobin Yang, Xiaolei Hou, Peng Carmo, Bruno Mao, Xuerui Fluid Dynamics Atmospheric and Oceanic Physics Data Analysis, Statistics and Probability Accurate, efficient prediction of wind flow with wake effects is crucial for wind-farm layout and power forecasting. Existing approaches-physical measurements, numerical simulations, physics-based models, and data-driven models-face trade-offs: the first two are time- and resource-intensive; physics-based models can lack accuracy due to limited physics; data-driven methods leverage abundant, high-quality data and are increasingly popular. We propose a rapid, data-driven wake-flow model inspired by video-frame interpolation and the principle of similarity. Field data are transformed into images; multi-scale feature recognition then identifies, matches, and interpolates wake structures using Scale-Invariant Feature Transform (SIFT) and Dynamic Time Warping (DTW) to generate intermediate flow fields. Six representative mini wind-farm cases validate the approach, spanning variations in turbine spacing, turbine size, combined spacing-size variations, different turbine counts, and wind-direction misalignment. Across cases, the method achieves a mean absolute percentage error (MAPE) of 0.68-2.28%. Because it flexibly computes both 2D and 3D wake fields, the method offers substantial computational-efficiency gains over large-eddy simulation (LES) and Meteodyn WT when 2D accuracy suffices for industrial needs. Accordingly, it provides a practical alternative to measurements, high-fidelity simulations, and simplified physics-based models, enabling efficient expansion of wake-flow databases for wind-farm design and power prediction while balancing speed and accuracy. |
| title | Data-driven modeling of wind farm wake flow based on multi-scale feature recognition |
| topic | Fluid Dynamics Atmospheric and Oceanic Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2509.21005 |