Wake behind an offshore wind farm observed with dual-Doppler radars

Nygaard, Nicolai; Newcombe, Alexander

doi:10.1088/1742-6596/1037/7/072008

Cited by 57 publications

(77 citation statements)

References 19 publications

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“…Wind farm operational data was coupled with wind fields derived from dual-Doppler (two synchronized Doppler radars) measurements to further investigate wind farm wake effects and evaluate wake modelling [25]. Dual-Doppler (DD) radar measurements of the wake behind an offshore wind farm were also reported in [26]. Power performance studies [27] and ramp events detection [28] were also documented using DD radar measurements.In [29], the authors introduced a methodology that uses DD radar measurements as input for predicting the average aggregated power of seven offshore wind turbines in a probabilistic framework.…”

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confidence: 99%

On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

et al. 2018

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Very short-term forecasts of wind power provide electricity market participants with extremely valuable information, especially in power systems with high penetration of wind energy. In very short-term horizons, statistical methods based on historical data are frequently used. This paper explores the use of dual-Doppler radar observations of wind speed and direction to derive five-minute ahead deterministic and probabilistic forecasts of wind power. An advection-based technique is introduced, which estimates the predictive densities of wind speed at the target wind turbine. In a case study, the proposed methodology is used to forecast the power generated by seven turbines in the North Sea with a temporal resolution of one minute. The radar-based forecast outperforms the persistence and climatology benchmarks in terms of overall forecasting skill. Results indicate that when a large spatial coverage of the inflow of the wind turbine is available, the proposed methodology is also able to generate reliable density forecasts. Future perspectives on the application of Doppler radar observations for very short-term wind power forecasting are discussed in this paper. methods can be found in [6][7][8]. Very short-term forecasts of wind speed and power are often based on machine learning methods such as artificial neural networks [9] or Markov chain models [10,11], which are trained with historical data. Alternatively, combinations of different models (known as hybrid models) have been proposed to overcome the deficiencies of single models [12]. Parallel to the development of single point or deterministic forecasting models, the use of probabilistic forecasts, which include information about the uncertainty associated with the predicted events, has increased. Examples of very short-term probabilistic forecasts of wind power using statistical methods can be found in [13,14]. These probabilistic forecasts are known as predictive densities or probability distributions and provide important information for making risk-based decisions [15].Over the last two decades, the use of remote sensing measurements such as long-range lidars [16] has been extended in the wind industry. These systems are capable of measuring wind speed and direction (under certain assumptions) up to 30 km [17]. Unlike conventional wind measurements from met-mast or satellites, they present an adequate trade-off between temporal and spatial resolution for wind farm applications. However, the prediction horizon of a remote sensing-based forecasting model is limited by the maximum range of the remote sensing measurements and also influenced by meteorological conditions [18]. Indeed, publications on the use of long-range lidar measurements for wind energy applications have reported measurements with a maximum range of less than ten kilometers [19][20][21]. Despite those limitations, a recent contribution showed that a lidar-based forecasting technique can provide better results than conventional statistical benchmarks when forecasting near-coastal winds with le...

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confidence: 99%

On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

et al. 2018

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“…Finally, the present static model does not incorporate the influence of Coriolis forces which have been shown to play a role in the wakes of large wind farms in simulations [20] and field observations [16]. A basic model for the influence of Coriolis forces on the wakes of wind turbines was proposed by [13] but has not been validated in experimental studies and is left for future work.…”

Section: Physics-based Modelmentioning

confidence: 99%

“…Consider inflow from 330 • for example. It is likely that the wakes of Column B turbines will merge into a large wind farm wake [16]. This wake is then subject to large-scale dynamic wake meandering which may cause it to directly impinge or miss Column C entirely.…”

Section: Physics-based Modelmentioning

confidence: 99%

“…However, the aerodynamic interactions within wind farm models remain challenging to capture as a result of wake superposition [10], boundary layer interactions [6,11], complex terrain [12], Coriolis forces [13], deep array effects [14,15], and other neglected physics. When wind turbines are tightly spaced in a wind farm, the individual turbine wakes merge into a large wind farm wake [16]. Wind farm wakes may extend over tens of kilometers and have been shown to influence downwind wind farms [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

Howland

Dabiri

2019

Energies

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Turbulent wakes trailing utility-scale wind turbines reduce the power production and efficiency of downstream turbines. Thorough understanding and modeling of these wakes is required to optimally design wind farms as well as control and predict their power production. While low-order, physics-based wake models are useful for qualitative physical understanding, they generally are unable to accurately predict the power production of utility-scale wind farms due to a large number of simplifying assumptions and neglected physics. In this study, we propose a suite of physics-informed statistical models to accurately predict the power production of arbitrary wind farm layouts. These models are trained and tested using five years of historical one-minute averaged operational data from the Summerview wind farm in Alberta, Canada. The trained models reduce the prediction error compared both to a physics-based wake model and a standard two-layer neural network. The trained parameters of the statistical models are visualized and interpreted in the context of the flow physics of turbulent wind turbine wakes.

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“…Therefore, field flow measurements around utility-scale turbines are indispensable to bridge the gap by offering first-hand data for improving the design of laboratory experiments and numerical models used in wind turbine research. Although the state-of-the-art field flow diagnostic techniques such as lidar, radar and sodar provide powerful tools to quantify the wind field across a wind farm scale [9][10][11][12], their spatial and temporal resolutions are still quite limited when applied to measure the turbulent flows around individual turbines. This limitation becomes a major hurdle, particularly for the flow characterization in the near wake region of the turbine.…”

Section: Introductionmentioning

confidence: 99%

Snow-powered research on utility-scale wind turbine flows

Hong

Abraham

2020

Acta Mech. Sin.

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This paper provides a review of the general experimental methodology of snow-powered flow visualization and super-large-scale particle imaging velocimetry (SLPIV), the corresponding field deployments and major scientific findings from our work on a 2.5 MW utility-scale wind turbine at the Eolos field station. The field measurements were conducted to investigate the incoming flow in the induction zone and the near-wake flows from different perspectives. It has been shown that these snowpowered measurements can provide sufficient spatiotemporal resolution and fields of view to characterize both qualitatively and quantitatively the incoming flow, all the major coherent structures generated by the turbine (e.g., blade, nacelle and tower vortices, etc.) as well as the development and interaction of these structures in the near wake. Our work has further revealed several interesting behaviors of near-wake flows (e.g., wake contraction, dynamic wake modulation, and meandering and deflection of nacelle wake, etc.), and their connections with constantly-changing inflows and turbine operation, which are uniquely associated with utility-scale turbines. These findings have demonstrated that the near wake flows, though highly complex, can be predicted with substantial statistical confidence using SCADA and structural response information readily available from the current utility-scale turbines. Such knowledge can be potentially incorporated into wake development models and turbine controllers for wind farm optimization in the future.

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Wake behind an offshore wind farm observed with dual-Doppler radars

Cited by 57 publications

References 19 publications

On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

Snow-powered research on utility-scale wind turbine flows

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