Minute‐scale detection and probabilistic prediction of offshore wind turbine power ramps using dual‐Doppler radar

Valldecabres, Laura; Bremen, Lueder von; Kühn, Martin

doi:10.1002/we.2553

Cited by 10 publications

(19 citation statements)

References 41 publications

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“…For the evaluation of the probabilistic wind power forecasts, we distinguished between stable or neutral and unstable atmospheric stratification. Situations with values of −1000 m < L < 0 m were classified as unstable, while those with 0 m < L < 1000 m were defined as stable (Van Wijk et al, 1990). All other cases were defined as neutral.…”

Section: Resultsmentioning

confidence: 99%

Minute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurements

Theuer

Dooren

Bremen³

et al. 2020

Wind Energ. Sci.

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Abstract. Decreasing gate closure times on the electricity stock exchange market and the rising share of renewables in today's energy system causes an increasing demand for very short-term power forecasts. While the potential of dual-Doppler radar data for that purpose was recently shown, the utilization of single-Doppler lidar measurements needs to be explored further to make remote-sensing-based very short-term forecasts more feasible for offshore sites. The aim of this work was to develop a lidar-based forecasting methodology, which addresses a lidar's comparatively low scanning speed. We developed a lidar-based forecast methodology using horizontal plan position indicator (PPI) lidar scans. It comprises a filtering methodology to recover data at far ranges, a wind field reconstruction, a time synchronization to account for time shifts within the lidar scans and a wind speed extrapolation to hub height. Applying the methodology to seven free-flow turbines in the offshore wind farm Global Tech I revealed the model's ability to outperform the benchmark persistence during unstable stratification, in terms of deterministic as well as probabilistic scores. The performance during stable and neutral situations was significantly lower, which we attribute mainly to errors in the extrapolation of wind speed to hub height.

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Section: Resultsmentioning

confidence: 99%

Minute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurements

Theuer

Dooren

Bremen³

et al. 2020

Wind Energ. Sci.

Self Cite

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“…Probabilistic forecasts, which also provide information about the uncertainty of the forecast, have gained increasing traction due to the additional support they provide to decision-makers [23][24][25]. Recent studies presented a probabilistic framework to forecast offshore wind power generation using dual radar measurements [26,27]. Reference [26] focused on predicting power generation five minutes ahead for a small number of turbines (first wind-facing row of the wind farm) under a precise set of conditions (wind speed less than 16 ms −1 and wind direction between 191 and 282 • ).…”

Section: Wind Field Remote Sensing For Wind Power Forecastingmentioning

confidence: 99%

“…Reference [26] focused on predicting power generation five minutes ahead for a small number of turbines (first wind-facing row of the wind farm) under a precise set of conditions (wind speed less than 16 ms −1 and wind direction between 191 and 282 • ). Building on this approach, Reference [27] extended the methodology to the entire wind farm by including efficiency correction factors for wake-affected turbines. The method could outperform the probabilistic persistence benchmark during specific ramp events, but was unsuccessful for longer assessment periods.…”

Section: Wind Field Remote Sensing For Wind Power Forecastingmentioning

confidence: 99%

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Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR

et al. 2021

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It remains unclear to what extent remote sensing instruments can effectively improve the accuracy of short-term wind power forecasts. This work seeks to address this issue by developing and testing two novel forecasting methodologies, based on measurements from a state-of-the-art long-range scanning Doppler LiDAR. Both approaches aim to predict the total power generated at the wind farm scale with a five minute lead time and use successive low-elevation sector scans as input. The first approach is physically based and adapts the solar short-term forecasting approach referred to as “smart-persistence” to wind power forecasting. The second approaches the same short-term forecasting problem using convolutional neural networks. The two methods were tested over a 72 day assessment period at a large wind farm site in Victoria, Australia, and a novel adaptive scanning strategy was implemented to retrieve high-resolution LiDAR measurements. Forecast performances during ramp events and under various stability conditions are presented. Results showed that both LiDAR-based forecasts outperformed the persistence and ARIMA benchmarks in terms of mean absolute error and root-mean-squared error. This study is therefore a proof-of-concept demonstrating the potential offered by remote sensing instruments for short-term wind power forecasting applications.

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“…The spacing of wind turbines is determined by the terrain of the site and the influence of wind turbines on each other (their wake). Wakes cause energy losses through reduced wind speeds and, at the same time, greater power output fluctuations and loads through increased turbulence (Crespo and Hernàndez, 1996;Vermeer et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data

et al. 2021

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Abstract. Space–time correlations of power output fluctuations of wind turbine pairs provide information on the flow conditions within a wind farm and the interactions of wind turbines. Such information can play an essential role in controlling wind turbines and short-term load or power forecasting. However, the challenges of analysing correlations of power output fluctuations in a wind farm are the highly varying flow conditions. Here, we present an approach to investigate space–time correlations of power output fluctuations of streamwise-aligned wind turbine pairs based on high-resolution supervisory control and data acquisition (SCADA) data. The proposed approach overcomes the challenge of spatially variable and temporally variable flow conditions within the wind farm. We analyse the influences of the different statistics of the power output of wind turbines on the correlations of power output fluctuations based on 8 months of measurements from an offshore wind farm with 80 wind turbines. First, we assess the effect of the wind direction on the correlations of power output fluctuations of wind turbine pairs. We show that the correlations are highest for the streamwise-aligned wind turbine pairs and decrease when the mean wind direction changes its angle to be more perpendicular to the pair. Further, we show that the correlations for streamwise-aligned wind turbine pairs depend on the location of the wind turbines within the wind farm and on their inflow conditions (free stream or wake). Our primary result is that the standard deviations of the power output fluctuations and the normalised power difference of the wind turbines in a pair can characterise the correlations of power output fluctuations of streamwise-aligned wind turbine pairs. Further, we show that clustering can be used to identify different correlation curves. For this, we employ the data-driven k-means clustering algorithm to cluster the standard deviations of the power output fluctuations of the wind turbines and the normalised power difference of the wind turbines in a pair. Thereby, wind turbine pairs with similar power output fluctuation correlations are clustered independently from their location. With this, we account for the highly variable flow conditions inside a wind farm, which unpredictably influence the correlations.

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Minute‐scale detection and probabilistic prediction of offshore wind turbine power ramps using dual‐Doppler radar

Cited by 10 publications

References 41 publications

Minute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurements

Minute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurements

Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR

Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data

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