Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy

Doekemeijer, Bart; Kern, Stefan; Maturu, Sivateja; Kanev, Stoyan; Salbert, Bastian; Schreiber, Johannes; Campagnolo, Filippo; Bottasso, Carlo L.; Schuler, Simone; Wilts, Friedrich; Neumann, Thomas; Potenza, Giancarlo; Calabretta, Fabio; Fioretti, Federico; Wingerden, Jan‐Willem van

doi:10.5194/wes-2020-80

Cited by 18 publications

(32 citation statements)

References 8 publications

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“…Further, using CFD simulations, Cossu (2021) demonstrates that re-optimizing the blade pitch angle of a yawed wind turbine in below-rated operation can both reduce the power loss from yaw misalignment and increase the power gain from wake steering at downstream turbines. Through a wake-steering field experiment, Doekemeijer et al (2021) observe asymmetry in the power loss as a function of yaw misalignment, similar to the findings of Howland et al (2020), while also noting a relatively flat peak of the power curve as a function of yaw offset followed by a sharp drop in power production for more extreme offsets.…”

Section: Impact Of Yaw Misalignment On Power Productionsupporting

confidence: 69%

“…Large increases in power -as well as significant reductions in the variability of the power production -were achieved at low wind speeds because wake steering caused the waked turbines to shut down less frequently by maintaining wind speeds above the cut-in speed. Last, Doekemeijer et al (2021) described a wake-steering experiment at a wind plant in Italy in which two turbines were controlled to improve the net power production for either a row of three turbines or pairs of turbines spaced 5.2D to 6.5D apart, depending on the wind direction. Using both positive and negative yaw offsets, the authors observed increases in energy production of up to 35 % for the two-turbine scenario and 16 % for the row of three turbines while also acknowledging net losses in energy production for certain wind directions.…”

Section: Introductionmentioning

confidence: 99%

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Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Simley

Fleming

Girard³

et al. 2021

Wind Energ. Sci.

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Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

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Section: Impact Of Yaw Misalignment On Power Productionsupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Simley

Fleming

Girard³

et al. 2021

Wind Energ. Sci.

View full text Add to dashboard Cite

show abstract

“…Details of cross‐stream components of wake velocity have been examined by Martínez‐Tossas et al, 11 in which a ‘curled‐wake’ model is formulated, and by Shapiro et al, 12 in which yawed turbines are viewed as lifting lines. In recent developments, several wind tunnel studies (for example, 13‐16 ), numerical simulations (for example, 17‐20 ), as well as field studies (for example, 21‐26 ) have examined wake steering for wind farms of single‐rotor wind turbines, in which improvements have been seen in terms of both power output and reliability.…”

Section: Introductionmentioning

confidence: 99%

Wake steering of multirotor wind turbines

et al. 2021

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In this paper, wake steering is applied to multirotor turbines to determine whether it has the potential to reduce wind plant wake losses. Through application of rotor yaw to multirotor turbines, a new degree of freedom is introduced to wind farm control such that wakes can be expanded, channelled or redirected to improve inflow conditions for downstream turbines. Five different yaw configurations are investigated (including a baseline case) by employing large‐eddy simulations (LES) to generate a detailed representation of the velocity field downwind of a multirotor wind turbine. Two lower‐fidelity models from single‐rotor yaw studies (curled‐wake model and analytical Gaussian wake model) are extended to the multirotor case, and their results are compared with the LES data. For each model, the wake is analysed primarily by examining wake cross‐sections at different downwind distances. Further quantitative analysis is carried out through characterisations of wake centroids and widths over a range of streamwise locations and through a brief analysis of power production. Most significantly, it is shown that rotor yaw can have a considerable impact on both the distribution and magnitude of the wake velocity deficit, leading to power gains for downstream turbines. The lower‐fidelity models show small deviation from the LES results for specific configurations; however, both are able to reasonably capture the wake trends over a large streamwise range.

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“…The P p exponent depends on the time-varying inflow. Additional inaccuracies arise in the cosine model since the power production as a function of the yaw misalignment is not generally symmetric in non-uniform flow (Howland et al, 2020d;Doekemeijer et al, 2021). Numerical experiments in Part 1 (Howland et al, 2020c) demonstrated that underestimating P p leads to poor wake steering performance.…”

Section: Model-based Closed-loop Wake Steering Control Methodology Updatesmentioning

confidence: 99%

Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions

Howland

Ghate

Quesada

et al. 2021

Preprint

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Abstract. The magnitude of wake interactions between individual wind turbines depends on the atmospheric stability. We investigate strategies for wake loss mitigation through the use of closed-loop wake steering using large eddy simulations of the diurnal cycle, where variations in the surface heat flux in time modify the atmospheric stability, wind speed and direction, shear, turbulence, and other atmospheric boundary layer flow (ABL) features. The closed-loop wake steering control methodology developed in Part 1 (Howland et al., Wind Energy Science, 2020, 5, 1315–1338) is implemented in an eight turbine wind farm in large eddy simulations of the diurnal cycle. The optimal yaw misalignment set-points depend on the wind direction, which varies in time during the diurnal cycle. To improve the application of wake steering control in transient ABL conditions with an evolving mean flow state, we develop a regression-based wind direction forecast method. We compare the closed-loop wake steering control methodology to baseline yaw aligned control and open-loop lookup table control for various selections of the yaw misalignment set-point update frequency, which dictates the balance between wind direction tracking and yaw activity. Closed-loop wake steering with set-point optimization under uncertainty results in higher collective energy production than both baseline yaw aligned control and open-loop lookup table control. The increase in wind farm energy production for closed- and open-loop wake steering control compared to baseline yaw aligned control, is 4.0–4.1 % and 3.4–3.8 %, respectively, with the range indicating variations in the energy increase results depending on the set-point update frequency. The primary energy increases through wake steering occur during stable ABL conditions. Open-loop lookup table control decreases energy production in the convective ABL conditions simulated, compared to baseline yaw aligned control, while closed-loop control increases energy production in convective conditions.

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Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy

Cited by 18 publications

References 8 publications

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Wake steering of multirotor wind turbines

Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions

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