Collective and nonlinear structure of wind power correlations
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866916067371122688 |
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| author | Lakhal, Samy E. Sardonia, J. E. Bandi, M. M. |
| author_facet | Lakhal, Samy E. Sardonia, J. E. Bandi, M. M. |
| contents | We describe the correlation structure of wind power fluctuations in a farm of 80 turbines, sampled over 5 years. We report the presence of universal, collective, and nonlinear correlations, responsible for the excess persistency and intermittency of farm-aggregated power output. A first cross-correlation analysis of turbine production reveals a dynamical scaling transition (à la Family-Vicszek) from local decoherence to large-scale turbulence-driven scaling, and responsible for the geographical smoothing effect, previously reported beyond farm scale [M. M. Bandi, Phys. Rev. Lett. 118, 028301 (2017)]. A second bivariate analysis shows the long-range correlation of non-Gaussian features, responsible for their amplification in total farm output. These findings provide a new perspective on wind power variability, highlighting the importance of nonlinear correlations in power production dynamics. By better characterising these fluctuations, our results can inform strategies for grid management, storage optimization, and wind farm design, ultimately improving the integration of wind energy into modern power systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10136 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Collective and nonlinear structure of wind power correlations Lakhal, Samy E. Sardonia, J. E. Bandi, M. M. Fluid Dynamics Atmospheric and Oceanic Physics Data Analysis, Statistics and Probability We describe the correlation structure of wind power fluctuations in a farm of 80 turbines, sampled over 5 years. We report the presence of universal, collective, and nonlinear correlations, responsible for the excess persistency and intermittency of farm-aggregated power output. A first cross-correlation analysis of turbine production reveals a dynamical scaling transition (à la Family-Vicszek) from local decoherence to large-scale turbulence-driven scaling, and responsible for the geographical smoothing effect, previously reported beyond farm scale [M. M. Bandi, Phys. Rev. Lett. 118, 028301 (2017)]. A second bivariate analysis shows the long-range correlation of non-Gaussian features, responsible for their amplification in total farm output. These findings provide a new perspective on wind power variability, highlighting the importance of nonlinear correlations in power production dynamics. By better characterising these fluctuations, our results can inform strategies for grid management, storage optimization, and wind farm design, ultimately improving the integration of wind energy into modern power systems. |
| title | Collective and nonlinear structure of wind power correlations |
| topic | Fluid Dynamics Atmospheric and Oceanic Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2602.10136 |