Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

B. M. Doekemeijer, S. Boersma, L. Y. Pao, T. Knudsen, J.-W. van Wingerden

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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Resumé

Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified model is calibrated and used for optimization in real time. This paper presents a joint state-parameter estimation solution with an ensemble Kalman filter at its core, which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Exclusively using measurements of each turbine's generated power, the adaptability to modeling errors and mismatches in atmospheric conditions is shown. Convergence is reached within 400s of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately 2 orders of magnitude faster.
OriginalsprogEngelsk
TidsskriftWind Energy Science
Vol/bind3
Udgave nummer2
Sider (fra-til)749-765
Antal sider17
DOI
StatusUdgivet - 24 okt. 2018

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Farms
Calibration
Kalman filters
Wind turbines
Parameter estimation
Error analysis
Program processors
Dynamic models
Flow fields
Turbines
Costs

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Doekemeijer, B. M. ; Boersma, S. ; Pao, L. Y. ; Knudsen, T. ; van Wingerden, J.-W. / Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control. I: Wind Energy Science. 2018 ; Bind 3, Nr. 2. s. 749-765.
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title = "Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control",
abstract = "Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified model is calibrated and used for optimization in real time. This paper presents a joint state-parameter estimation solution with an ensemble Kalman filter at its core, which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Exclusively using measurements of each turbine's generated power, the adaptability to modeling errors and mismatches in atmospheric conditions is shown. Convergence is reached within 400s of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately 2 orders of magnitude faster.",
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Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control. / Doekemeijer, B. M.; Boersma, S.; Pao, L. Y.; Knudsen, T.; van Wingerden, J.-W.

I: Wind Energy Science, Bind 3, Nr. 2, 24.10.2018, s. 749-765.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

AU - Doekemeijer, B. M.

AU - Boersma, S.

AU - Pao, L. Y.

AU - Knudsen, T.

AU - van Wingerden, J.-W.

PY - 2018/10/24

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N2 - Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified model is calibrated and used for optimization in real time. This paper presents a joint state-parameter estimation solution with an ensemble Kalman filter at its core, which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Exclusively using measurements of each turbine's generated power, the adaptability to modeling errors and mismatches in atmospheric conditions is shown. Convergence is reached within 400s of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately 2 orders of magnitude faster.

AB - Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified model is calibrated and used for optimization in real time. This paper presents a joint state-parameter estimation solution with an ensemble Kalman filter at its core, which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Exclusively using measurements of each turbine's generated power, the adaptability to modeling errors and mismatches in atmospheric conditions is shown. Convergence is reached within 400s of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately 2 orders of magnitude faster.

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