Towards uncovering the structure of power fluctuations of wind farms

Liu, Huiwen; Jin, Yaqing; Tobin, Nicolas; Chamorro, Leonardo P.

doi:10.1103/physreve.96.063117

Cited by 37 publications

(44 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After all, it is not possible to respect the atmospheric turbulence through Richards and Norris turbulence boundary conditions and to obtain realistic turbulence behavior on the HAWT blade with the common two‐equation turbulence model in one U‐RANS computation at the same time. The purpose of the U‐RANS computation has either to be the precise prediction of the wake recovery process by defining the turbulence according to Richards and Norris or of the rotor loads in an ABL inflow profile by artificially fixing the turbulence quantities …”

Section: Resultsmentioning

confidence: 99%

Impact of wall roughness and turbulence level on the performance of a horizontal axis wind turbine with the U‐RANS solver THETA

Länger-Möller¹

2019

Wind Energy

View full text Add to dashboard Cite

The presented work investigates the impact of different sheared velocity profiles in the atmospheric boundary layer on the characteristics of a wind turbine by modifying the wall roughness coefficients in the logarithmic velocity profile. Moreover, the rotor and wake characteristics in dependence of the turbulence boundary conditions are investigated. In variant I, the turbulence boundary conditions are defined in accordance to the logarithmic velocity profile with different wall roughness lengths. In variant II, the turbulent kinetic energy and turbulent viscosity remain independent of the velocity profile and represent the free‐stream turbulence level. With an increase of the shear in the velocity profile, the amplitudes in the 3/rev characteristics of rotor thrust and rotor torque, induction factors, and effective angles of attack are increased. In variant I, the overall levels of thrust coefficient are hardly affected by the velocity profiles resulting from different wall roughness length values. The power coefficient is reduced about 1%. Conversely, compared with variant II, a difference of 2% in the power coefficient has been detected. Moreover, the wake recovery process strongly depends on the turbulence boundary condition. Simulations are carried out on an industrial 900‐kW wind turbine with the incompressible U‐RANS solver THETA.

show abstract

Section: Resultsmentioning

confidence: 99%

Impact of wall roughness and turbulence level on the performance of a horizontal axis wind turbine with the U‐RANS solver THETA

Länger-Möller¹

2019

Wind Energy

View full text Add to dashboard Cite

show abstract

“…The first region, at frequencies 1< f / f 0 <10 3 , is similar to the inertial range in Kolmogorov's spectra, with the energy decaying as f −5/3 . As suggested by previous studies, the power variation can be expressed as

P^{'} = C U^{'} false(t false) + scriptO false(U^{'} {false(t false)}^{2}, U^{'} {false(t false)}^{3} false),

where the prime symbol (') denotes fluctuation and

C = 3 false/ 2 ρ A C_{P} {\overset{U}{true}}^{2}

. Therefore, the fluctuation of the power is a linear function of velocity fluctuations plus higher‐order terms that can be neglected as an approximation.…”

Section: Power Production and Its Variabilitymentioning

confidence: 98%

“…Spectra of the fluctuation of the power production; A, comparison of (_________) individual turbines and (_ _ _ _) cumulative power spectra: () WRF D 5 domain, () domain D 6− H R (UTD‐WF); B, comparison of the spectra of the power production of the cumulative power against the analytical models of () Liu et al in equation and () Bossuyt et al [Colour figure can be viewed at wileyonlinelibrary.com]…”

Section: Power Production and Its Variabilitymentioning

confidence: 99%

One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data

et al. 2020

View full text Add to dashboard Cite

One-way nested mesoscale to microscale simulations of an onshore wind farm have been performed nesting the Weather Research and Forecasting (WRF) model and our in-house high-resolution large-eddy simulation code (UTD-WF). Each simulation contains five nested WRF domains, with the largest domain spanning the North Texas Panhandle region with a 4 km resolution, while the highest resolution (50 m) nest simulates microscale wind fluctuations and turbine wakes within a single wind farm. The finest WRF domain in turn drives the UTD-WF LES higher-resolution domain for a subset of six turbines at a resolution of ∼ 5 m. The wind speed, direction, and boundary layer profiles from WRF are compared against measurements obtained with a met-tower and a scanning Doppler wind LiDAR located within the wind farm. Additionally, power production obtained from WRF and UTD-WF are assessed against supervisory control and data acquisition (SCADA) system data. Numerical results agree well with the experimental measurements of the wind speed, direction, and power production of the turbines. UTD-WF high-resolution domain improves significantly the agreement of the turbulence intensity at the turbines location compared with that of WRF. Velocity spectra have been computed to assess how the nesting allows resolving a wide range of scales at a reasonable computational cost. A domain sensitivity analysis has been performed. Velocity spectra indicate that placing the inlet too close to the first row of turbines results in an unrealistic peak of energy at the rotational frequency of the turbines. Spectra of the power production of a single turbine and of the cumulative power of the array have been compared with analytical models.

show abstract

“…Except flow velocity distributions shown in Figure 15, turbulent intensity is another index of evaluating upstream flow quality in the water or wind tunnel. This is related to the fact that turbulent fluctuations are important in determining the performance of the rotor [50]. In addition, with existing experimental facilities and apparatus, static pressure distribution near the rotor cannot be obtained [51].…”

Section: Upstream Flow In Tunnelsmentioning

confidence: 98%

Upstream Flow Control for the Savonius Rotor under Various Operation Conditions

et al. 2018

View full text Add to dashboard Cite

Applications of the Savonius rotor have been extended in recent years, necessitating an in-depth investigation on flow characteristics of such a fluid energy converting device. For the wake flow downstream of the Savonius rotor, studies have been reported extensively. Nevertheless, literature specifically devoted to the upstream flow of the Savonius rotor can rarely be found. This review collects and compiles findings from relevant studies to prove the significance of upstream flow patterns to the operation of the Savonius rotor. Then attempts from experimental and numerical aspects to substantiate the important effect of the upstream flow are implemented. Based on practical cases and laboratory works, upstream flow patterns for the Savonius rotor are divided into four types, namely uniform flow, guided flow, rotor wake flow and oscillating flow. Accordingly, conditions under which these upstream flow patterns arise are analyzed respectively. Experimental and numerical results are presented to clarify the influential factors underlying diverse upstream flow patterns. Furthermore, the relationship between the performance of the Savonius and the upstream flow is elucidated, facilitating the development of techniques of controlling the upstream flow. This review provides a systematic reference for the control of the upstream flow for the Savonius rotor, which has the tendency of developing into an independent technical branch.

show abstract

Towards uncovering the structure of power fluctuations of wind farms

Cited by 37 publications

References 50 publications

Impact of wall roughness and turbulence level on the performance of a horizontal axis wind turbine with the U‐RANS solver THETA

Impact of wall roughness and turbulence level on the performance of a horizontal axis wind turbine with the U‐RANS solver THETA

One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data

Upstream Flow Control for the Savonius Rotor under Various Operation Conditions

Contact Info

Product

Resources

About