Development of a high fidelity CFD code for wind farm control

Santoni, Christian; Ciri, Umberto; Rotea, Mario A.; Leonardi, Stefano

doi:10.1109/acc.2015.7170980

Cited by 40 publications

(36 citation statements)

References 4 publications

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“…In this paper, the performance of NESC is evaluated using large‐eddy simulations (LESs), conducted with our in‐house code University of Texas at Dallas‐Wind Farm model (UTD‐WF) . Large‐eddy simulations reproduce a three‐turbine wind farm, the Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas .…”

Section: Introductionmentioning

confidence: 99%

Effect of the turbine scale on yaw control

2018

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Yaw misalignment between the incoming wind and the rotor of a turbine causes a lateral displacement of the wake. This effect can be exploited to avoid or mitigate wake interactions in wind farms, so that power losses are minimized. We performed large‐eddy simulations to evaluate yaw control for a three‐turbine wind farm. We used two different turbine models to assess how the size of the turbine rotor affects the farm efficiency and the effectiveness of the control strategy. A utility‐scale wind turbine with rotor diameter of 126 m is compared with a scaled research wind turbine with rotor diameter of 27 m. In both cases, a model‐free algorithm is used to determine the turbine yaw set point, which maximizes total power production. The algorithm is the nested extremum‐seeking control (NESC), which allows for the coordinated optimization of the wind turbine operating points. The results achieved with NESC are validated by computing a static performance map for different yaw angles. NESC converges to optimal operating conditions, which are in good agreement with the static map benchmark. Numerical results show that a larger rotor diameter induces larger wake deflection, thus achieving higher power improvements. From the analysis of the turbine structural loads, an increase in damage equivalent load is observed for both the yawed turbine and the waked one. Present results suggest that there is a cost‐effective trade‐off between performance and loads for large turbines.

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Section: Introductionmentioning

confidence: 99%

Effect of the turbine scale on yaw control

2018

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“…The capability of accurately predicting evolution of wind turbine wakes, their interactions and effects on power performance is important for optimization of wind farm layout , formulation of more accurate predictions of annual energy production from a wind power plant and control of wind turbine operations . Specifically, improved control strategies of wind farms have been proposed for minimizing wake interactions, such as by derating upstream wind turbines, thus enabling larger available power and reduced wake turbulence for downstream waked turbines , or by steering upstream wakes .…”

Section: Introductionmentioning

confidence: 99%

Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data

2017

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Power production of an onshore wind farm is investigated through supervisory control and data acquisition data, while the wind field is monitored through scanning light detection and ranging measurements and meteorological data acquired from a met‐tower located in proximity to the turbine array. The power production of each turbine is analysed as functions of the operating region of the power curve, wind direction and atmospheric stability. Five different methods are used to estimate the potential wind power as a function of time, enabling an estimation of power losses connected with wake interactions. The most robust method from a statistical standpoint is that based on the evaluation of a reference wind velocity at hub height and experimental mean power curves calculated for each turbine and different atmospheric stability regimes. The synergistic analysis of these various datasets shows that power losses are significant for wind velocities higher than cut‐in wind speed and lower than rated wind speed of the turbines. Furthermore, power losses are larger under stable atmospheric conditions than for convective regimes, which is a consequence of the stability‐driven variability in wake evolution. Light detection and ranging measurements confirm that wind turbine wakes recover faster under convective regimes, thus alleviating detrimental effects due to wake interactions. For the wind farm under examination, power loss due to wake shadowing effects is estimated to be about 4% and 2% of the total power production when operating under stable and convective conditions, respectively. However, cases with power losses about 60‐80% of the potential power are systematically observed for specific wind turbines and wind directions. Copyright © 2017 John Wiley & Sons, Ltd.

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“…Santoni et al [27] calculated the optimum tip speed ratio (referred in their work as Jensen optimal point) for each turbine in a three-turbine configuration for power maximization using Jensen's model [28] and blade element theory. They also performed a large-eddy simulation of the same turbine arrangement using an immersed boundary method framework coupled with actuator line methods (ALM) for turbine modeling.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Wake Mitigation through Blade Pitch Offset

Dilip

Porté‐Agel

2017

Energies

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Abstract:The reduction in power output associated with complex turbine-wake interactions in wind farms necessitates the development of effective wake mitigation strategies. One approach to this end entails the downregulation of individual turbines from its maximum power point with the objective of optimizing the overall wind farm productivity. Downregulation via blade pitch offset has been of interest as a potential strategy, though the viability of this method is still not clear, especially in regard to its sensitivity to ambient turbulence. In this study, large-eddy simulations of a two-turbine arrangement, with the second turbine in the full wake of the first, were performed. The effects of varying the blade pitch angle of the upstream turbine on its wake characteristics, as well as the combined power of the two, were investigated. Of specific interest was the effect of turbulence intensity of the inflow on the efficacy of this method. Results showed enhanced wake recovery associated with pitching to stall, as opposed to pitching to feather, which delayed wake recovery. The increased wake recovery resulted in a noticeable increase in the power of the two-turbine configuration, only in conditions characterized by low turbulence in the incoming flow. Nevertheless, the low turbulence scenarios where the use of this method is favorable, are expected in realistic wind farms, suggesting its possible application for improved power generation.

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Development of a high fidelity CFD code for wind farm control

Cited by 40 publications

References 4 publications

Effect of the turbine scale on yaw control

Effect of the turbine scale on yaw control

Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data

Wind Turbine Wake Mitigation through Blade Pitch Offset

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