Nonlinear System Identification for Model-Based Control of Waked Wind Turbines
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911199281545216 |
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| author | Randino, Sebastiano Schena, Lorenzo Coudou, Nicolas Garone, Emanuele Mendez, Miguel Alfonso |
| author_facet | Randino, Sebastiano Schena, Lorenzo Coudou, Nicolas Garone, Emanuele Mendez, Miguel Alfonso |
| contents | This work presents a nonlinear system identification framework for modeling the power extraction dynamics of wind turbines, including both freestream and waked conditions. The approach models turbine dynamics using data-driven power coefficient maps expressed as combinations of compact radial basis functions and polynomial bases, parameterized in terms of tip-speed ratio and upstream conditions. These surrogate models are embedded in a first-order dynamic system suitable for model-based control. Experimental validation is carried out in two wind tunnel configurations: a low-turbulence tandem setup and a high-turbulence wind farm scenario. In the tandem case, the identified model is integrated into an adapted Kω^2 controller, resulting in improved tip-speed ratio tracking and power stability compared to BEM-based and steady-state models. In the wind farm scenario, the model captures the statistical behavior of the turbines despite unresolved turbulence. The proposed method enables interpretable, adaptive control across a range of operating conditions without relying on black-box learning strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07336 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nonlinear System Identification for Model-Based Control of Waked Wind Turbines Randino, Sebastiano Schena, Lorenzo Coudou, Nicolas Garone, Emanuele Mendez, Miguel Alfonso Systems and Control Fluid Dynamics This work presents a nonlinear system identification framework for modeling the power extraction dynamics of wind turbines, including both freestream and waked conditions. The approach models turbine dynamics using data-driven power coefficient maps expressed as combinations of compact radial basis functions and polynomial bases, parameterized in terms of tip-speed ratio and upstream conditions. These surrogate models are embedded in a first-order dynamic system suitable for model-based control. Experimental validation is carried out in two wind tunnel configurations: a low-turbulence tandem setup and a high-turbulence wind farm scenario. In the tandem case, the identified model is integrated into an adapted Kω^2 controller, resulting in improved tip-speed ratio tracking and power stability compared to BEM-based and steady-state models. In the wind farm scenario, the model captures the statistical behavior of the turbines despite unresolved turbulence. The proposed method enables interpretable, adaptive control across a range of operating conditions without relying on black-box learning strategies. |
| title | Nonlinear System Identification for Model-Based Control of Waked Wind Turbines |
| topic | Systems and Control Fluid Dynamics |
| url | https://arxiv.org/abs/2510.07336 |