Analytical model for the power-yaw sensitivity of wind turbines operating in full wake

Liew, Jaime; Urbán, Albert M.; Andersen, Søren Juhl

doi:10.5194/wes-2019-65

Cited by 10 publications

(17 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The uncertainties and sensitivities in this study associated with the wall model, subfilter scale model, wind turbine model, and ABL characteristics such as boundary layer inversion height were not investigated in detail and are left for future work. More reliable and generalizable estimates for P p (Liew et al, 2019), or generally C p as a function of γ, should be investigated. Future work should also investigate the influence of latitude and geostrophic wind direction on wake steering control performance (Howland et al, 2020a).…”

Section: Discussionmentioning

confidence: 99%

“…However, simulations have shown for the NREL 5 MW turbine that P p = 1.88 (Gebraad et al, 2016a). Recent work has shown that P p differs for freestream and waked turbines (Liew et al, 2019). The value of P p that results in a satisfactory agreement with experimental data depends on the wind turbine model, ABL shear and veer, and atmospheric stability.…”

Section: Lifting Line Wake Modelmentioning

confidence: 97%

See 1 more Smart Citation

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Howland¹,

Ghate²,

Lele³

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. Strategies for wake loss mitigation through the use of dynamic closed-loop wake steering are investigated using large eddy simulations of conventionally neutral atmospheric boundary layer conditions, where the neutral boundary layer is capped by an inversion and a stable free atmosphere. The closed-loop controller synthesized in this study consists of a physics-based lifting line wake model combined with a data-driven Ensemble Kalman filter state estimation technique to calibrate the wake model as a function of time in a generalized transient atmospheric flow environment. Computationally efficient gradient ascent yaw misalignment selection along with efficient state estimation enables the dynamic yaw calculation for real-time wind farm control. The wake steering controller is tested in a six turbine array embedded in a quasi-stationary conventionally neutral flow with geostrophic forcing and Coriolis effects included. The controller increases power production compared to baseline, greedy, yaw-aligned control although the magnitude of success of the controller depends on the state estimation architecture and the wind farm layout. The influence of the model for the coefficient of power Cp as a function of the yaw misalignment is characterized. Errors in estimation of the power reduction as a function of yaw misalignment are shown to result in yaw steering configurations that under-perform the baseline yaw aligned configuration. Overestimating the power reduction due to yaw misalignment leads to increased power over greedy operation while underestimating the power reduction leads to decreased power, and therefore, in an application where the influence of yaw misalignment on Cp is unknown, a conservative estimate should be taken. Sensitivity analyses on the controller architecture, coefficient of power model, wind farm layout, and atmospheric boundary layer state are performed to assess benefits and trade-offs in the design of a wake steering controller for utility-scale application. The physics-based wake model with data assimilation predicts the power production in yaw misalignment with a mean absolute error over the turbines in the farm of 0.02 P1, with P1 as the power of the leading turbine at the farm, whereas a physics-based wake model with wake spreading based on an empirical turbulence intensity relationship leads to a mean absolute error of 0.11 P1.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Lifting Line Wake Modelmentioning

confidence: 97%

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Howland¹,

Ghate²,

Lele³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…5. The reduction in the cumulative power output is even more pronounced at large yaw angles of the downstream turbine, ψ 2 , as the power decrease for a yawed turbine operating in wake is increased compared to a freestanding turbine; see Liew et al (2019). Furthermore, the effect of wake deflection is clearly visible in Fig.…”

Section: Wake Deflection and Velocity Deficitmentioning

confidence: 91%

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

2020

Self Cite

View full text Add to dashboard Cite

Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high-fidelity flow simulations coupled with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. The wind farm control strategy is optimized for both the power output and the loading of the turbines. The optimization performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggests that a power gain of almost 3%±1% can be achieved at close spacing by yawing the upstream turbine more than 15∘. At larger spacing the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction in the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, as well as the implications for wind farm control.

show abstract

“…The potential for wake steering to increase wind farm power production depends on the magnitude of wake interactions between the wind turbines, the magnitude of the wake deflection as a function of yaw misalignment, and the power production lost by the yaw misaligned turbines 10 . The power production of a wind turbine in yaw misalignment is often modeled 3,11 as…”

Section: Introductionmentioning

confidence: 99%

Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment

Howland

Gonzalez

Martínez

et al. 2020

Journal of Renewable and Sustainable Energy

View full text Add to dashboard Cite

The intentional yaw misalignment of leading, upwind turbines in a wind farm, termed wake steering, has demonstrated potential as a collective control approach for wind farm power maximization. The optimal control strategy and the resulting effect of wake steering on wind farm power production are in part dictated by the power degradation of the upwind yaw misaligned wind turbines. In the atmospheric boundary layer, the wind speed and direction may vary significantly over the wind turbine rotor area, depending on atmospheric conditions and stability, resulting in freestream turbine power production which is asymmetric as a function of the direction of yaw misalignment and which varies during the diurnal cycle. In this study, we propose a model for the power production of a wind turbine in yaw misalignment based on aerodynamic blade elements, which incorporates the effects of wind speed and direction changes over the turbine rotor area in yaw misalignment. The proposed model can be used for the modeling of the angular velocity, aerodynamic torque, and power production of an arbitrary yaw misaligned wind turbine based on the incident velocity profile, wind turbine aerodynamic properties, and turbine control system. A field experiment is performed using multiple utility-scale wind turbines to characterize the power production of yawed freestream operating turbines depending on the wind conditions, and the model is validated using the experimental data. The resulting power production of a yaw misaligned variable speed wind turbine depends on a nonlinear interaction between the yaw misalignment, the atmospheric conditions, and the wind turbine control system.

show abstract

Analytical model for the power-yaw sensitivity of wind turbines operating in full wake

Cited by 10 publications

References 20 publications

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment

Contact Info

Product

Resources

About