Characterizing power performance and wake of a wind turbine under yaw and blade pitch

Marathe, Neha; Swift, Allegra; Hirth, Brian D.; Walker, R.P.; Schroeder, John L.

doi:10.1002/we.1875

Cited by 37 publications

(24 citation statements)

References 11 publications

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“…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. This paper extends the methodology presented in [29] to predict the power generated by individual wind turbines and analyses the characteristics of DD radar measurements for very short-term forecasts of wind power.…”

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confidence: 99%

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

et al. 2018

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“…Meteorological conditions also impact wind turbine wake structure. Although little is known how local ABL heterogeneities and other coherent turbulent structures modulate the wind turbine wake, previous research suggests downstream wake progression might be modified by transient ABL streak features (Marathe et al, 2016). Therefore, further analysis was performed to examine how ABL streaks, specifically streak orientation, might have promoted the unexpected values of θ V skew relative to θ V err .…”

Section: Impact Of Wind Turbine Yaw Error On Downstream Wake Locationmentioning

confidence: 99%

Exploring the complexities associated with full-scale wind plant wake mitigation control experiments

Hirth

Schroeder

2020

Wind Energ. Sci.

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Recent research promotes implementing next-generation wind plant control methods to mitigate turbine-to-turbine wake effects. Numerical simulation and wind tunnel experiments have previously demonstrated the potential benefit of wind plant control for wind plant optimization, but full-scale validation of the wake-mitigating control strategies remains limited. As part of this study, the yaw and blade pitch of a utilityscale wind turbine were strategically modified for a limited time period to examine wind turbine wake response to first-order turbine control changes. Wind turbine wake response was measured using Texas Tech University's Ka-band Doppler radars and dual-Doppler scanning strategies. Results highlight some of the complexities associated with executing and analyzing wind plant control at full scale using brief experimental control periods. Some difficulties include (1) the ability to accurately implement the desired control changes, (2) identifying reliable data sources and methods to allow these control changes to be accurately quantified, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions (e.g., boundary layer streak orientation, atmospheric stability). To better understand wake sensitivity to the underlying atmospheric conditions, wake evolution within the early-evening transition was also examined using a single-Doppler data collection approach. Analysis of both wake length and meandering during this period of transitioning atmospheric stability indicates the potential benefit and feasibility of wind plant control should be enhanced when the atmosphere is stable.Published by Copernicus Publications on behalf of the European Academy of Wind Energy e.V.

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“…As a result the velocity deficit at the right part of the wake increases, so the wake deflects to the right. Marathe et al (2016) could show in their field measurement campaign with a dual-Doppler radar the wake drifting to the right. But in the far wake they registered a movement to the left.…”

Section: The Wake Modelmentioning

confidence: 99%

Monitoring offshore wind farm power performance with SCADA data and an advanced wake model

Mittelmeier¹,

Blodau²,

Kühn³

2017

Wind Energ. Sci.

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Abstract. Wind farm underperformance can lead to significant losses in revenues. The efficient detection of wind turbines operating below their expected power output and immediate corrections help maximize asset value. The method, presented in this paper, estimates the environmental conditions from turbine states and uses precalculated lookup tables from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output ratio between two turbines are an indication of underperformance. The confidence of detected underperformance is estimated by a detailed analysis of the uncertainties of the method. Power normalization with reference turbines and averaging several measures performed by devices of the same type can reduce uncertainties for estimating the expected power. A demonstration of the method's ability to detect underperformance in the form of degradation and curtailment is given. An underperformance of 8 % could be detected in a triple-wake condition.

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Characterizing power performance and wake of a wind turbine under yaw and blade pitch

Cited by 37 publications

References 11 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

Exploring the complexities associated with full-scale wind plant wake mitigation control experiments

Monitoring offshore wind farm power performance with SCADA data and an advanced wake model

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