On the accuracy of a logarithmic extrapolation of the wind speed measured by horizontal lidar scans

Theuer, Frauke; Dooren, Marijn Floris van; Bremen, Lueder von; Kühn, Martin

doi:10.1088/1742-6596/1618/3/032043

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…Information regarding stability further allows us to analyse the effect of atmospheric conditions on global blockage. We used a similar methodology to derive atmospheric stratification as in Theuer et al (2020b) and Schneemann et al (2020), which is described here for completeness. To characterize atmospheric stability (Emeis, 2018), we used local measurements as well as reanalysis data.…”

Section: Atmospheric-stability Characterization and Meteorological Measurementsmentioning

confidence: 99%

“…The lidar measurements we analyse in this paper were originally performed to study the effect of cluster wakes in the inflow of GT I (Schneemann et al, 2020) and to perform minute-scale power forecasts (Theuer et al, 2020b). Due to the comparably small global-blockage effect, all errors influencing the accuracy of lidar measurements need to be carefully examined and reduced wherever possible.…”

Section: Global-blockage Measurement Techniquesmentioning

confidence: 99%

“…Commercial scanning lidar systems allow for measuring the line-of-sight (LOS) component of the wind vector on several hundred positions along the emitted laser beam and for orientating the beam in any direction. Scanning lidars have enabled many new insights into different fields of wind energy research, like wind turbine wakes (Käsler et al, 2010;Trabucchi et al, 2014), wind farm cluster wakes (Schneemann et al, 2020), resource assessment in complex terrain (Menke et al, 2020) and minutescale wind power forecasts (Theuer et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

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Offshore wind farm global blockage measured with scanning lidar

et al. 2021

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Abstract. The objective of this paper was the experimental investigation of the accumulated induction effect of a large offshore wind farm as a whole, i.e. the global-blockage effect, in relation to atmospheric-stability estimates and wind farm operational states. We measured the inflow of a 400 MW offshore wind farm in the German North Sea with a scanning long-range Doppler wind lidar. A methodology to reduce the statistical variability of different lidar scans at comparable measurement conditions was introduced, and an extensive uncertainty assessment of the averaged wind fields was performed to be able to identify the global-blockage effect, which is small compared to e.g. wind turbine wake effects and ambient variations in the inflow. Our results showed a 4 % decrease in wind speed (accuracy range of 2 % to 6 %) at transition piece height (24.6 m) upwind of the wind farm with the turbines operating at high thrust coefficients above 0.8 in a stably stratified atmosphere, which we interpreted as global blockage. In contrast, at unstable stratification and similar operating conditions and for situations with low thrust coefficients (i.e. approx. 0 for not operating turbines and ≤ 0.3 for turbines operating far above rated wind speed) we identified no wind speed deficit. We discussed the significance of our measurements and possible sources of error in long-range scanning lidar campaigns and give recommendations on how to measure small flow effects like global blockage with scanning Doppler lidar. In conclusion, we provide strong evidence for the existence of global blockage in large offshore wind farms in stable stratification and the turbines operating at a high thrust coefficient by planar lidar wind field measurements. We further conclude that global blockage is dependent on atmospheric stratification.

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Section: Atmospheric-stability Characterization and Meteorological Measurementsmentioning

confidence: 99%

Section: Global-blockage Measurement Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Offshore wind farm global blockage measured with scanning lidar

et al. 2021

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“…Persistence is often referred to as a benchmark in very short-term forecasting (Würth et al, 2019). Other statistical models such as ARMA (autoregressive moving average) take a higher number of past values and past forecasting errors into account (Torres et al, 2005). ARIMA (autoregressive integrated moving average) models additionally difference the time series to achieve stationarity (Grigonytė and Butkeviciūtė, 2016).…”

Section: Introductionmentioning

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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“…The basis of this work is the lidar-based forecasting approach introduced and analysed in more detail in Theuer et al (2020aTheuer et al ( , b, 2021. The method is briefly described in Sect.…”

Section: Methodsmentioning

confidence: 99%

Observer-based power forecast of individual and aggregated offshore wind turbines

Theuer

Rott²,

Schneemann³

et al. 2022

Wind Energ. Sci.

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Abstract. Due to the increasing share of wind energy in the power system, minute-scale wind power forecasts have gained importance. Remote-sensing-based approaches have proven to be a promising alternative to statistical methods and thus need to be further developed towards an operational use, aiming to increase their forecast availability and skill. Therefore, the contribution of this paper is to extend lidar-based forecasts to a methodology for observer-based probabilistic power forecasts of individual wind turbines and aggregated wind farm power. To do so, lidar-based forecasts are combined with supervisory control and data acquisition (SCADA)-based forecasts that advect wind vectors derived from wind turbine operational data. After a calibration, forecasts of individual turbines are aggregated to a probabilistic power forecast of turbine subsets by means of a copula approach. We found that combining the lidar- and SCADA-based forecasts significantly improved both forecast skill and forecast availability of a 5 min ahead probabilistic power forecast at an offshore wind farm. Calibration further increased the forecast skill. Calibrated observer-based forecasts outperformed the benchmark persistence for unstable atmospheric conditions. The aggregation of probabilistic forecasts of turbine subsets revealed the potential of the copula approach. We discuss the skill, robustness and dependency on atmospheric conditions of the individual forecasts, the value of the observer-based forecast, its calibration and aggregation, and more generally the value of minute-scale power forecasts of offshore wind. In conclusion, combining different data sources to an observer-based forecast is beneficial in all regarded cases. For an operational use one should distinguish between and adapt to atmospheric stability.

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On the accuracy of a logarithmic extrapolation of the wind speed measured by horizontal lidar scans

Cited by 4 publications

References 16 publications

Offshore wind farm global blockage measured with scanning lidar

Offshore wind farm global blockage measured with scanning lidar

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

Observer-based power forecast of individual and aggregated offshore wind turbines

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