Atmospheric stability and topography effects on wind turbine performance and wake properties in complex terrain

Han, Xingxing; Liu, Deyou; Xu, Chunxiao; Shen, Wen Zhong

doi:10.1016/j.renene.2018.03.048

Cited by 66 publications

(39 citation statements)

References 12 publications

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“…This wind farm is located in Northwest China, and has a total capacity of 50 MW with 25 turbines. This wind farm has been studied in [23,32] to investigate the wake flow simulation in complex terrain using CFD, and the preliminary results of the layout optimization study of this wind farm have been presented in [18]. Some of this wind farm's site conditions have already been shown in Figure 1.…”

Section: Case Study and Resultsmentioning

confidence: 99%

An Optimization Framework for Wind Farm Design in Complex Terrain

Feng

Shen

2018

Applied Sciences

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Designing wind farms in complex terrain is an important task, especially for countries with a large portion of complex terrain territory. To tackle this task, an optimization framework is developed in this study, which combines the solution from a wind resource assessment tool, an engineering wake model adapted for complex terrain, and an advanced wind farm layout optimization algorithm. Various realistic constraints are modelled and considered, such as the inclusive and exclusive boundaries, minimal distances between turbines, and specific requirements on wind resource and terrain conditions. The default objective function in this framework is the total net annual energy production (AEP) of the wind farm, and the Random Search algorithm is employed to solve the optimization problem. A new algorithm called Heuristic Fill is also developed in this study to find good initial layouts for optimizing wind farms in complex terrain. The ability of the framework is demonstrated in a case study based on a real wind farm with 25 turbines in complex terrain. Results show that the framework can find a better design, with 2.70% higher net AEP than the original design, while keeping the occupied area and minimal distance between turbines at the same level. Comparison with two popular algorithms (Particle Swarm Optimization and Genetic Algorithm) also shows the superiority of the Random Search algorithm.

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Section: Case Study and Resultsmentioning

confidence: 99%

An Optimization Framework for Wind Farm Design in Complex Terrain

Feng

Shen

2018

Applied Sciences

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“…In general, turbulence exchanges between the wake and the atmosphere are depressed under stable conditions. High wake deficits and slow wake recovery thus are usually observed in the stable stratification boundary layer [1,2]. Since wakes play critical roles in wind farm energy production and the fatigue loads of wind Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 16 August 2019 doi:10.20944/preprints201908.0175.v1…”

Section: Introductionmentioning

confidence: 99%

“…The impact of atmospheric stability on wakes are widely observed in wind tunnel measurements of small-scale models of wind turbines [3][4][5] and full-scale field experiments [1,2,[6][7][8]. According to the wind tunnel measurements in Chamorror et al [3], the stronger inlet wind shear in the stable case leads to a slightly stronger turbulence intensity and extends the region of enhanced turbulence intensity from a distance of about 4-5.5 rotor diameters to 3 and 6 rotor diameters downwind of the turbine location.…”

Section: Introductionmentioning

confidence: 99%

“…In Zhang et al [4], a 15% smaller velocity deficit at the wake center, a more rapid momentum recovery due to an enhanced radial momentum transport, a 20% higher peak turbulence intensity were observed in the unstable case, as compared to the wake of the same wind turbine under neutral conditions. According to the field experiments of wakes using LiDARs or masts, Magnusson et al [6], Iungo et al [7] and Han et al [2] also found that the velocity deficit decreases more slowly under stable conditions and more quickly under unstable conditions. Those observations suggest that atmospheric stability should be considered for improved wake models and predictions of wind power harvesting.…”

Section: Introductionmentioning

confidence: 99%

“…3). Measurements of the sonic anemometer installed on M1 at 30 m are applied to estimate the heat flux w θ , the Obukhov length L and the friction speed u *[2]. Effective data after considering data synchronization and sensor errors are about 190 days.…”

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confidence: 99%

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Monin-Obukhov Similarity Theory for Modeling of Wind Turbine Wakes under Atmospheric Stable Conditions: Breakdown and Modifications

Han

Liu

et al. 2019

Preprint

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Monin-Obukhov similarity theory (MOST) overestimates wind shear in some atmospheric stable conditions, i.e. Richardson number $R_f<0.25$. The overestimated wind shear that leads to an under-predicted friction wind speed and a lower ambient turbulence intensity for a given hub-height reference wind speed and a given roughness length, could influence wake modeling of a wind turbine. This work investigates the side effects of the breakdown of MOST on wake modeling under stable conditions and makes some modifications to the flow similarity functions to eliminate these side effects. Based on a field measurement in a wind farm, we firstly show that MOST predicts a larger wind shear for the atmospheric stability parameter $\zeta>0.1$ and proposes new flow similarity functions without constraining $R_f$ to limit the overestimated wind shear by MOST. Next, different turbulence models based on MOST and a modified one based on the new similarity functions are investigated through numerical simulations. These turbulence models are combined with the actuator disk model (AD) and Reynolds-averaged Navier–Stokes equations (RANS) to model wind turbine wakes under stable conditions. As compared to measurements, numerical results show that turbulence models based on MOST result in larger wake deficits and slower wake recovery rate with a square root of the mean-squared-error (RSME) of wake deficit in the range of 0.07-0.18. This overestimated wake effect is improved by applying the new similarity functions and the RSME of wake deficit is averagely reduced by 0.05. Finally, we check the role of the under-predicted turbulence intensity playing in the larger wake deficit predicted by models based MOST. Additional numerical simulations using the modified turbulence model are carried out, in which the roughness length is reduced to impose a hub-height ambient turbulence intensity equivalent to the MOST case. Simulation results show that reducing turbulence intensity enhances wake effects, however, it cannot reproduce the large wake deficit predicted by models based on MOST, which suggests that the overestimated wake effect by MOST could be also related to the overestimated wind shear.

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Deep generative model for probabilistic wind speed and wind power estimation at a wind farm

Salazar

Zheng

Che

et al. 2022

Energy Science & Engineering

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This work introduces a novel method to generate probabilistic hub‐height wind speed forecasts aimed at power output prediction. We employ state‐of‐the‐art convolutional variational autoencoders (CVAEs) trained with historical wind speed observations, multivariable outputs (wind speed, direction, temperature, pressure, and humidity) from a numerical weather prediction (NWP) model and spatio‐temporal encodings. After training, we exploit the CVAE data generating capabilities to produce probabilistic forecasts from the same deterministic dynamical NWP model. The resulting probabilistic forecast provides an insight into the uncertainty of the original deterministic input that compensate for errors due to numerical discretization, inaccuracies in initial/boundary conditions and parameterizations and, most importantly wind speed fluctuations due to complex terrain features. To show the performance of the proposed model, we validate the approach with forecasted and observed data for fifteen sites in a wind farm in Awaji Island, Japan, in a challenging zone with complex topography and highly fluctuating wind patterns. We show that the proposed method provides improved wind speed forecasts from both deterministic (reduced root mean square error) and probabilistic (reduced continuous ranked probability score) standpoints. We also use ensemble forecast validation methods to assess the statistical properties of the CVAE and conclude that in zones with rapidly changing wind speed dynamics, the CVAE ensemble performs in a superior way when compared to the analog ensemble method. Finally, we also provide an insight on how the results can be used to obtain reliable wind power estimations.

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Atmospheric stability and topography effects on wind turbine performance and wake properties in complex terrain

Cited by 66 publications

References 12 publications

An Optimization Framework for Wind Farm Design in Complex Terrain

An Optimization Framework for Wind Farm Design in Complex Terrain

Monin-Obukhov Similarity Theory for Modeling of Wind Turbine Wakes under Atmospheric Stable Conditions: Breakdown and Modifications

Deep generative model for probabilistic wind speed and wind power estimation at a wind farm

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