On the tails of the wind ramp distributions

DeMarco, Adam W.; Basu, Sukanta

doi:10.1002/we.2202

Cited by 12 publications

(11 citation statements)

References 40 publications

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“…This non-Gaussian behaviour is a characteristic of power increments and has been extensively studied. 47,48 Following the work of Wan on the ERCOT system, 46 we set the threshold C 0 to 3 , where is an estimate of the standard deviation of the distribution of 5-min changes in wind farm normalised power. This threshold corresponds to 10% of the nominal power of the wind farm.…”

Section: Ramp Definitionmentioning

confidence: 99%

“…The distribution has a median value close to zero and presents heavier tails than a Gaussian distribution, with some extreme values close to 50% of the wind farm's capacity. This non‐Gaussian behaviour is a characteristic of power increments and has been extensively studied 47,48 . Following the work of Wan on the ERCOT system, 46 we set the threshold C 0 to 3 σ , where σ is an estimate of the standard deviation of the distribution of 5‐min changes in wind farm normalised power.…”

Section: Detection and Characterisation Of Minute‐scale Ramp Eventsmentioning

confidence: 99%

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

Valldecabres

Bremen²,

Kühn

2020

Wind Energy

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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.

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Section: Ramp Definitionmentioning

confidence: 99%

Section: Detection and Characterisation Of Minute‐scale Ramp Eventsmentioning

confidence: 99%

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

Valldecabres

Bremen²,

Kühn

2020

Wind Energy

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“…It should be noted that this "peaky" characteristic or sharp cusp is already inherent in the inflow to the turbine. The experimental evidence in [41] from 4 wind measurement towers based in 4 different terrains (from coastal to complex) reveal similar shapes of the PDFs of their wind increments. Aside from the heavy tails of the distributions, at small time scales in particular, they reveal a "strong peakedness near the mode of the distribution".…”

Section: Results and Discussion On Impact Of Turbulence Intermittencymentioning

confidence: 83%

An investigation of the impact of turbulence intermittency on the rotor loads of a small wind turbine

Whale

Peinke

2021

Renewable Energy

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“…A representative value of the turbulence length scale L MM is also needed for constrained turbulence simulations. Since L MM is not expected to be a significant driver of loads due to the dominance of the ramps (it has less influence than σ u,hpf , as shown in, e.g., Dimitrov et al, 2018), it was calculated as L MM = σ u,hpf /(dU before /dz) upper following Kelly (2018) 5 . Table 1 shows the ensemble members and chosen characteristics.…”

Section: Ensemble Of Ramp Events For Coupled Simulationsmentioning

confidence: 99%

Statistical impact of wind-speed ramp events on turbines, via observations and coupled fluid-dynamic and aeroelastic simulations

2021

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Abstract. Via 11 years of high-frequency measurements, we calculated the probability space of expected offshore wind-speed ramps, recasting it compactly in terms of relevant load-driving quantities for horizontal-axis wind turbines. A statistical ensemble of events in reduced ramp-parameter space (ramp acceleration, mean speed after ramp, upper-level shear) was created to capture the variability of ramp parameters and also allow connection of such to ramp-driven loads. Constrained Mann-model (CMM) turbulence simulations coupled to an aeroelastic model were made for each ensemble member, for a single turbine. Ramp acceleration was found to dominate the maxima of thrust-associated loads, with a ramp-induced increase of 45 %–50 % for blade-root flap-wise bending moment and tower-base fore–aft moment, plus ∼ 3 % per 0.1 m/s2 of bulk ramp-acceleration magnitude. The ensemble of ramp events from the CMM was also embedded in large-eddy simulation (LES) of a wind farm consisting of rows of nine turbines. The LES uses actuator-line modeling for the turbines and is coupled to the aeroelastic model. The LES results indicate that the ramps, and the mean acceleration associated with them, tend to persist through the farm. Depending on the ramp acceleration, ramps crossing rated speed lead to maximum loads, which are nearly constant for the third row and further downwind. Where rated power is not achieved, the loads primarily depend on wind speed; as mean winds weaken within the farm, ramps can again have U < Vrated. This leads to higher loads than pre-ramp conditions, with the distance where loads begin to increase depending on inflow Umax⁡ relative to Vrated. For the ramps considered here, the effect of turbulence on loads is found to be small relative to ramp amplitude that causes Vrated to be exceeded, but for ramps with Uafter < Vrated, the combination of ramp and turbulence can cause load maxima. The same sensitivity of loads to acceleration is found in both the CMM-aeroelastic simulations and the coupled LES.

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On the tails of the wind ramp distributions

Cited by 12 publications

References 40 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

An investigation of the impact of turbulence intermittency on the rotor loads of a small wind turbine

Statistical impact of wind-speed ramp events on turbines, via observations and coupled fluid-dynamic and aeroelastic simulations

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