A control-oriented dynamic wind farm model: WFSim

Boersma, Sjoerd; Doekemeijer, Bart; Vali, Mehdi; Meyers, Johan; Wingerden, Jan‐Willem van

doi:10.5194/wes-3-75-2018

Cited by 97 publications

(89 citation statements)

References 36 publications

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“…On the other hand, model-free and classical control approaches have also received attention due to their simple control architecture and ease of implementation for real-time control of large wind farms (Marden et al, 2013;Gebraad and van Wingerden, 2015). Both allow the performance of the designed controllers to be evaluated with more realistic wind farm flow conditions, e.g., free field testing , wind tunnel testing (Campagnolo et al, 2016;Petrović et al, 2018), and high-fidelity LES models Ciri et al, 2017;Vali et al, 2018b). Extremum seeking control has been studied as a closed-loop realization of an optimization problem for power maximization of waked wind farms.…”

Section: Introductionmentioning

confidence: 99%

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An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer

et al. 2019

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Abstract. This paper studies a closed-loop wind farm control framework for active power control (APC) with a simultaneous reduction of wake-induced structural loads within a fully developed wind farm flow interacting with the atmospheric boundary layer. The main focus is on a classical feedback control, which features a simple control architecture and a practical measurement system that are realizable for real-time control of large wind farms. We demonstrate that the wake-induced structural loading of the downstream turbines can be alleviated, while the wind farm power production follows a reference signal. A closed-loop APC is designed first to improve the power-tracking performance against wake-induced power losses of the downwind turbines. Then, the nonunique solution of APC for the wind farm is exploited for aggregated structural load alleviation. The axial induction factors of the individual wind turbines are considered control inputs to limit the power production of the wind farm or to switch to greedy control when the demand exceeds the power available in the wind. Furthermore, the APC solution domain is enlarged by an adjustment of the power set-points according to the locally available power at the waked wind turbines. Therefore, the controllability of the wind turbines is improved for rejecting the intensified load fluctuations inside the wake. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures, and its interaction with the atmospheric boundary layer. The applicability and key features of the controller are discussed with a wind farm example consisting of 3×4 turbines with different wake interactions for each row. The performance of the proposed APC is evaluated using the accuracy of the wind farm power tracking and the wake-induced damage equivalent fatigue loads of the towers of the individual wind turbines.

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Section: Introductionmentioning

confidence: 99%

“…Therefore, a load balance within a wind farm may sacrifice the accuracy of the tracking performance or even instability. The yaw-based wake steering method might be employed to mitigate against such strong wake impacts on the wind farm power-tracking performance Boersma et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…On the other hand, model-free and classical control approaches have also received attention due to their simple control architecture and ease of implementation for real-time control of large wind farms (Marden et al, 2013;Gebraad and van Wingerden, 2015). Both allow the performance of the designed controllers to be evaluated with more realistic wind farm flow conditions, e.g., free field testing (Fleming et al, 2017), wind tunnel testing (Campagnolo et al, 2016;Petrović et al, 2018), and high-fidelity LES models (van Wingerden et al, 2017;Ciri et al, 2017;Vali et al, 2018b). Extremum seeking control has been studied as a closed-loop realization of an optimization problem for power maximization of waked wind farms.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Responses to Referee #2

Vali¹

2019

Preprint

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This paper studies a closed-loop wind farm control framework for active power control (APC) with a simultaneous reduction of wake-induced structural loads within a fully developed wind farm flow interacting with the atmospheric boundary layer. The main focus is on a classical feedback control, which features a simple control architecture and a practical measurement system that are realizable for real-time control of large wind farms. We demonstrate that the wake-induced structural loading of the downstream turbines can be alleviated, while the wind farm power production follows a reference signal. A closed-loop APC is designed first to improve the power-tracking performance against wake-induced power losses of the downwind turbines. Then, the nonunique solution of APC for the wind farm is exploited for aggregated structural load alleviation. The axial induction factors of the individual wind turbines are considered control inputs to limit the power production of the wind farm or to switch to greedy control when the demand exceeds the power available in the wind. Furthermore, the APC solution domain is enlarged by an adjustment of the power set-points according to the locally available power at the waked wind turbines. Therefore, the controllability of the wind turbines is improved for rejecting the intensified load fluctuations inside the wake. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures, and its interaction with the atmospheric boundary layer. The applicability and key features of the controller are discussed with a wind farm example consisting of 3 × 4 turbines with different wake interactions for each row. The performance of the proposed APC is evaluated using the accuracy of the wind farm power tracking and the wake-induced damage equivalent fatigue loads of the towers of the individual wind turbines.

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“…Furthermore, time-ahead predictions with these models are limited to the steady state, limiting their use for APC. There is a smaller yet significant number of dynamic surrogate wind farm models (e.g., Munters and Meyers, 2017;Boersma et al, 2018;Shapiro et al, 2017a) that attempt to include the dominant temporal dynamics inside the farm. These models can be used for control on the seconds timescale, and furthermore allow timeahead predictions, some even under changing atmospheric conditions.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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 400 s of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2 s 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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A control-oriented dynamic wind farm model: WFSim

Cited by 97 publications

References 36 publications

An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer

An active power control approach for wake-induced load alleviation in a fully developed wind farm boundary layer

Responses to Referee #2

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

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