Verification and Calibration of a Reduced Order Wind Farm Model by Wind Tunnel Experiments

Schreiber, Johannes; Nanos, Emmanouil M.; Campagnolo, Filippo; Bottasso, Carlo L.

doi:10.1088/1742-6596/854/1/012041

Cited by 30 publications

(35 citation statements)

References 5 publications

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“…The surrogate model for the closed-loop controller in this work is described in Section II. [22] and Annoni et al [23] have demonstrated this validation procedure for simplified steady-state surrogate models. This step is considered to be out of the scope of this paper for the presented case study.…”

Section: Introductionmentioning

confidence: 99%

A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model

Doekemeijer

Wingerden

Fleming

2019

2019 American Control Conference (ACC)

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In wind farms, wake interaction leads to losses in power capture and accelerated structural degradation when compared to freestanding turbines. One method to reduce wake losses is by misaligning the rotor with the incoming flow using its yaw actuator, thereby laterally deflecting the wake away from downstream turbines. However, this demands an accurate and computationally tractable model of the wind farm dynamics. This problem calls for a closed-loop solution. This tutorial paper fills the scientific gap by demonstrating the full closed-loop controller synthesis cycle using a steady-state surrogate model. Furthermore, a novel, computationally efficient and modular communication interface is presented that enables researchers to straight-forwardly test their control algorithms in large-eddy simulations. High-fidelity simulations of a 9-turbine farm show a power production increase of up to 11% using the proposed closed-loop controller compared to traditional, greedy wind farm operation.

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Section: Introductionmentioning

confidence: 99%

A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model

Doekemeijer

Wingerden

Fleming

2019

2019 American Control Conference (ACC)

View full text Add to dashboard Cite

show abstract

“…Therefore, first the employed model for wake deflection (Section 2.1) and an analytical description of the statistics of wind direction variability (Section 2.2) are introduced. Based on this, an optimization of the yaw angle adjustments of all turbines in a wind farm with respect to the relative change in the power output of the farm is established (Section 2.3) and evaluated for a fictitious 25 reference wind farm (Section 2.4).…”

Section: Methodsmentioning

confidence: 99%

“…The applicability of the model has been demonstrated in high-fidelity simulations (e.g., (Gebraad et al, 2016)), wind tunnel tests (Schreiber et al, 2017), and even to some degree field tests (Fleming et al,10 2017).…”

Section: Wake Deflection Modelmentioning

confidence: 99%

Robust active wake control in consideration of wind direction variability and uncertainty

Rott¹,

Doekemeijer²,

Seifert³

et al. 2018

Preprint

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Abstract. The prospects of active wake deflection control to mitigate wake-induced power losses in wind farms have been demonstrated by large eddy simulations, wind tunnel experiments and recent field tests. However, it has not yet been fully understood how the yaw control of wind farms should take into account the variability of current environmental conditions in the field and the uncertainty of their measurements. This research investigated the influence of dynamic wind direction changes on active wake deflection by intended yaw misalignment. For this purpose the wake model FLORIS was used together with 5 wind direction measurements recorded at an onshore met mast in flat terrain. The analysis showed that active wake deflection has a high sensitivity towards short-term wind directional changes. This can lead to an increased yaw activity of the turbines.Fluctuations and uncertainties can cause the attempt to increase the power output to fail. Therefore a methodology to optimise the yaw control algorithm for active wake deflection was introduced, which considers dynamic wind direction changes and inaccuracies in the determination of the wind direction. The evaluation based on real wind direction time series confirmed that 10 the robust control algorithm can be tailored to specific meteorological and wind farm conditions and that it can indeed achieve an overall power increase in realistic inflow conditions. Furthermore recommendations for the implementation are given which could combine the robust behaviour with reduced yaw activity.

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“…Thus, wind farm control algorithms synthesized using such models neglect any transient dynamics in the wind farm, thereby potentially limiting performance. While some of these models have shown success in wind tunnel tests (e.g., Schreiber et al, 2017) and field tests (e.g., Fleming et al, 2017a, b) for power maximization, the actuation frequency is limited to the minutes-scale, since the flow and turbine dynamics are predicted on the minute-scale. Furthermore,5 time-ahead predictions with these models are limited to the time-invariant steady-state, limiting their use for APC.…”

Section: Introductionmentioning

confidence: 99%

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

Doekemeijer¹,

Boersma²,

Pao³

et al. 2018

Preprint

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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 flow dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified dynamical LES model is calibrated and used for optimization in real time. This paper presents an estimation solution with an Ensemble Kalman filter (EnKF) at its core, 5 which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Using exclusively turbine SCADA measurements, the adaptability to modeling errors and changes in atmospheric conditions (TI, wind speed) is shown. Convergence is reached within 400 seconds 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 two orders of magnitude faster. Using 10 the calibration solution presented, the surrogate model can be used for accurate forecasting and optimization.

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Verification and Calibration of a Reduced Order Wind Farm Model by Wind Tunnel Experiments

Cited by 30 publications

References 5 publications

A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model

A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model

Robust active wake control in consideration of wind direction variability and uncertainty

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

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