An analysis of offshore wind farm SCADA measurements to identify 
key parameters influencing the magnitude of wake effects

Mittelmeier, Niko; Allin, Julian; Blodau, Tomas; Trabucchi, Davide; Steinfeld, Gerald; Rott, Andreas; Kühn, Martin

doi:10.5194/wes-2016-60

Cited by 5 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only wind speed levels at which wakes are developed have been considered. A similar analysis can be found in the work of Mittermeier et al 41 For WT12, the figure shows especially strong wake effects for the sectors 150°, 240° and 330°. For WT19, strong effects can be observed for 150°, whereas from W to N directions, WT19 is a free‐flow wind turbine.…”

Section: Remote Sensing‐based Wind Power Probabilistic Forecasting Toolsupporting

confidence: 87%

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

Valldecabres

Bremen²,

Kühn

2020

Wind Energy

Self Cite

View full text Add to dashboard Cite

Predicting the occurrence of strong and sudden variations in wind power, so-called ramp events, has become one of the main challenges for the operation of power systems with large shares of wind power. In this paper, we investigate 14 ramp events of different magnitudes and minute-scale durations observed by a dual-Doppler radar system at the Westermost Rough offshore wind farm. The identified ramps are characterised using radar observations, turbine data and data from the Weather Research and Forecasting (WRF) model. A remote sensing-based forecasting methodology that propagates wind speeds upstream of wake-free turbines is extended here to the whole farm, by including corrections for wake effects. The methodology aims to probabilistically forecast the wind turbines' power in the form of density forecasts. The ability to predict ramp events of different magnitudes is evaluated and compared with probabilistic statistical and physical benchmarks. During the observed ramp events, the remote sensing-based forecasting model strongly outperforms the benchmarks. We show here that remote sensing observations such as radar data can significantly enhance very short-term forecasts of wind power.

show abstract

Section: Remote Sensing‐based Wind Power Probabilistic Forecasting Toolsupporting

confidence: 87%

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

Valldecabres

Bremen²,

Kühn

2020

Wind Energy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Higher fluctuations of the power in the neutral cases can be observed, corresponding to the higher TI that is present in the neutral stratification (cf. Mittelmeier et al, 2017). The simulation data show a comparable behaviour with lower fluctuating power in the stable cases than in the neutral ones.…”

Section: Comparison Of the Turbine Datamentioning

confidence: 59%

Validation of a coupled atmospheric–aeroelastic model system for wind turbine power and load calculations

et al. 2022

Self Cite

View full text Add to dashboard Cite

Abstract. The optimisation of the power output of wind turbines requires the consideration of various aspects including turbine design, wind farm layout and more. An improved understanding of the interaction of wind turbines with the atmospheric boundary layer is an essential prerequisite for such optimisations. With numerical simulations, a variety of different situations and turbine designs can be compared and evaluated. For such a detailed analysis, the output of an extensive number of turbine and flow parameters is of great importance. In this paper a coupling of the aeroelastic code FAST (fatigue, aerodynamics, structures, and turbulence) and the large-eddy simulation tool PALM (parallelised large-eddy simulation model) is presented. The advantage of the coupling of these models is that it enables the analysis of the turbine behaviour, among others turbine power, blade and tower loads, under different atmospheric conditions. The proposed coupling is tested with the generic National Renewable Energy Laboratory (NREL) 5 MW turbine and the operational eno114 3.5 MW turbine. Simulating the NREL 5 MW turbine allows for a first evaluation of our PALM–FAST coupling approach based on characteristics of the NREL turbine reported in the literature. The basic test of the coupling with the NREL 5 MW turbine shows that the power curve obtained is very close to the one when using FAST alone. Furthermore, a validation with free-field measurement data for the eno114 3.5 MW turbine for a site in northern Germany is performed. The results show a good agreement with the free-field measurement data. Additionally, our coupling offers an enormous reduction of the computing time in comparison to an actuator line model, in one of our cases by 89 %, and at the same time an extensive output of turbine data.

show abstract

“…Higher fluctuations of the power in the neutral cases can be observed, corresponding to the higher TI that is present in the neutral stratification, c.f. (Mittelmeier et al, 2017). The simulation data shows a comparable behaviour with lower fluctuating power in the stable cases than in the neutral ones.…”

Section: Comparison Of the Turbine Datamentioning

confidence: 59%

Validation of a coupled atmospheric-aeroelastic model system for wind turbine power and load calculations

Krüger

Steinfeld

Kraft

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. The optimisation of the power output of wind turbines requires the consideration of various aspects including turbine design, wind farm layout and more. An improved understanding of the interaction of wind turbines with the atmospheric boundary layer is an essential prerequisite for such optimisations. Using numerical simulations, a variety of different situations and turbine designs can be compared and evaluated. For such a detailed analysis, the output of an extensive number of turbine and flow parameters is of great importance. Usually simulations are either specified to the output of turbine parameters or the detailed simulation of the flow. In this paper a coupling of the aeroelastic code FAST and the Large-Eddy Simulation tool PALM is presented. The advantage of the coupling of these models is that it enables the analysis of the turbine behaviour, i.a. turbine power, blade and tower loads, under different atmospheric conditions. The proposed coupling is tested with the generic NREL 5 MW turbine and the operational eno114 3.5 MW turbine. Simulating the NREL 5 MW turbine allows for a first evaluation of our PALM-FAST-coupling approach based on characteristics of the NREL turbine reported in the literature. The comparisons of the simulations to the NREL literature values show very promising results. Furthermore, a validation with free-field measurement data for the eno114 3.5 MW turbine for a site in Northern Germany is performed. The results show a good agreement with the free field measurement data. Additionally, our coupling offers an enormous reduction of the computing time, in comparison to similar methods with the same detail, and at the same time an extensive output of the turbine data.

show abstract

An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects

Cited by 5 publications

References 5 publications

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

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

Validation of a coupled atmospheric–aeroelastic model system for wind turbine power and load calculations

Validation of a coupled atmospheric-aeroelastic model system for wind turbine power and load calculations

Contact Info

Product

Resources

About