Field Implementation and Trial of Coordinated Control of WIND Farms

Ahmad, Tanvir; Coupiac, Olivier; Petit, Arthur; Guignard, Sophie; Girard, Nicolas; Kazemtabrizi, Behzad; Matthews, Peter

doi:10.1109/tste.2017.2774508

Cited by 30 publications

(32 citation statements)

References 9 publications

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“…As wind direction changes or wind speed increases further, gains in power production obtained through the upstream wind turbine down-regulation quickly drop to zero, underlining the limited applicability of an axial induction strategy for power production optimization. Moreover, these gains have to be taken cautiously since some studies have already underlined discrepancies between model predictions and actual power productions of wind turbines, especially when variation of the wake decay constant due to changes in c T was not taken into account (Annoni et al, 2016). These results are in line with the performed uncertainty assessment of the tuned Jensen model, where the uncertainty is shown to be more than the predicted power increase.…”

Section: Discussionsupporting

confidence: 67%

“…It should be noted that the tuning of the wake decay constant is based on TI only. Although TI is probably the most critical parameter, some studies underline the link between the variation of c T and k w (Bastankhah and Porté-Agel, 2014;Annoni et al, 2016). Accordingly, in Sect.…”

Section: Tuning Of the Modelmentioning

confidence: 98%

“…An intentionally high level of curtailment was applied on a wind turbine of the farm so that changes in its emitted wake could be observed from the analysis of downstream active power data. Even though no increase in aggregated power production was expected, a first analysis of wind turbine power production in the farm showed that part of the lost power at the upstream turbine could be retrieved downstream (Ahmad et al, 2017;Duc, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

Duc¹,

Coupiac²,

Girard³

et al. 2019

Wind Energ. Sci.

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Abstract. In this paper, a new calculation procedure to improve the accuracy of the Jensen wake model for operating wind farms is proposed. In this procedure, the wake decay constant is updated locally at each wind turbine based on the turbulence intensity measurement provided by the nacelle anemometer. This procedure was tested against experimental data at the Sole du Moulin Vieux (SMV) onshore wind farm in France and the Horns Rev-I offshore wind farm in Denmark. Results indicate that the wake deficit at each wind turbine is described more accurately than when using the original model, reducing the error from 15 % to 20 % to approximately 5 %. Furthermore, this new model properly calibrated for the SMV wind farm is then used for coordinated control purposes. Assuming an axial induction control strategy, and following a model predictive approach, new power settings leading to an increased overall power production of the farm are derived. Power gains found are on the order of 2.5 % for a two-wind-turbine case with close spacing and 1 % to 1.5 % for a row of five wind turbines with a larger spacing. Finally, the uncertainty of the updated Jensen model is quantified considering the model inputs. When checked against the predicted power gain, the uncertainty of the model estimations is seen to be excessive, reaching approximately 4 %, which indicates the difficulty of field observations for such a gain. Nevertheless, the optimized settings are to be implemented during a field test campaign at SMV wind farm in the scope of the national project SMARTEOLE.

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Section: Discussionsupporting

confidence: 67%

Section: Tuning Of the Modelmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

Duc¹,

Coupiac²,

Girard³

et al. 2019

Wind Energ. Sci.

Self Cite

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“…The limiting impact of wake effects can be mitigated through coordinated control of turbines' production within the wind farm. A practical demonstration of coordinated control based on coefficient of power (C P ) through pitch control was presented in [1]. Optimal curtailment of upstream turbines with optimised pitching can increase net production of a given wind farm [1].…”

Section: Introductionmentioning

confidence: 99%

“…A practical demonstration of coordinated control based on coefficient of power (C P ) through pitch control was presented in [1]. Optimal curtailment of upstream turbines with optimised pitching can increase net production of a given wind farm [1]. Another possible decision variable for coordinated control is the yaw offset (α), applied on an upstream turbine.…”

Section: Introductionmentioning

confidence: 99%

Implementation and Analyses of Yaw Based Coordinated Control of Wind Farms

et al. 2019

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This paper presents, with a live field experiment, the potential of increasing wind farm power generation by optimally yawing upstream wind turbine for reducing wake effects as a part of the SmartEOLE project. Two 2MW turbines from the Le Sole de Moulin Vieux (SMV) wind farm are used for this purpose. The upstream turbine (SMV6) is operated with a yaw offset ( α ) in a range of − 12 ° to 8° for analysing the impact on the downstream turbine (SMV5). Simulations are performed with intelligent control strategies for estimating optimum α settings. Simulations show that optimal α can increase net production of the two turbines by more than 5%. The impact of α on SMV6 is quantified using the data obtained during the experiment. A comparison of the data obtained during the experiment is carried out with data obtained during normal operations in similar wind conditions. This comparison show that an optimum or near-optimum α increases net production by more than 5% in wake affected wind conditions, which is in confirmation with the simulated results.

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Review of wake management techniques for wind turbines

Houck

2021

Wind Energy

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Summary The progression of wind turbine technology has led to wind turbines being incredibly optimized machines often approaching their theoretical maximum production capabilities. When placed together in arrays to make wind farms, however, they are subject to wake interference that greatly reduces downstream turbines' power production, increases structural loading and maintenance, reduces their lifetimes, and ultimately increases the levelized cost of energy. Development of techniques to manage wakes and operate larger and larger arrays of turbines more efficiently is now a crucial field of research. Herein, four wake management techniques in various states of development are reviewed. These include axial induction control, wake steering, the latter two combined, and active wake control. Each of these is reviewed in terms of its control strategies and use for power maximization, load reduction, and ancillary services. By evaluating existing research, several directions for future research are suggested.

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Field Implementation and Trial of Coordinated Control of WIND Farms

Cited by 30 publications

References 9 publications

Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

Implementation and Analyses of Yaw Based Coordinated Control of Wind Farms

Review of wake management techniques for wind turbines

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