Survey of modelling methods for wind turbine wakes and wind farms

Crespo, António; Hernández, J.; Frandsen, Sten Tronæs

doi:10.1002/(sici)1099-1824(199901/03)2:1<1::aid-we16>3.0.co;2-7

Cited by 414 publications

(243 citation statements)

References 25 publications

Supporting

Mentioning

229

Contrasting

Unclassified

Order By: Relevance

“…The PARK model developed by Jensen (1983) and the eddy viscosity model developed by Ainslie (1988) are commonly used, and a comprehensive review of wake modeling can be found in Crespo et al (1999). Such approaches decouple the wind flow calculation from the wake calculation and use wake models calibrated to a single turbine in isolation.…”

Section: Wind Plant Flow Modelingmentioning

confidence: 99%

Optimization of wind plant layouts using an adjoint approach

et al. 2017

View full text Add to dashboard Cite

Abstract. Using adjoint optimization and three-dimensional steady-state Reynolds-averaged Navier-Stokes (RANS) simulations, we present a new gradient-based approach for optimally siting wind turbines within utilityscale wind plants. By solving the adjoint equations of the flow model, the gradients needed for optimization are found at a cost that is independent of the number of control variables, thereby permitting optimization of large wind plants with many turbine locations. Moreover, compared to the common approach of superimposing prescribed wake deficits onto linearized flow models, the computational efficiency of the adjoint approach allows the use of higher-fidelity RANS flow models which can capture nonlinear turbulent flow physics within a wind plant. The steady-state RANS flow model is implemented in the Python finite-element package FEniCS and the derivation and solution of the discrete adjoint equations are automated within the dolfin-adjoint framework. Gradient-based optimization of wind turbine locations is demonstrated for idealized test cases that reveal new optimization heuristics such as rotational symmetry, local speedups, and nonlinear wake curvature effects. Layout optimization is also demonstrated on more complex wind rose shapes, including a full annual energy production (AEP) layout optimization over 36 inflow directions and 5 wind speed bins.

show abstract

Section: Wind Plant Flow Modelingmentioning

confidence: 99%

Optimization of wind plant layouts using an adjoint approach

et al. 2017

View full text Add to dashboard Cite

show abstract

“…It is often not possible to measure the wind speed and direction at the hub height. Computer simulation tools are developed to evaluate the wind conditions at hub height over a certain area from a point wind measurement with the local influences and obstacles [156][157][158].…”

Section: Wind Measurementmentioning

confidence: 99%

Progress and recent trends in wind energy

Sahin

2004

Progress in Energy and Combustion Science

275

100

View full text Add to dashboard Cite

“…Applying them to non-uniform flow field will lead to additional error. Crespo et al has developed the UPMWAKE model and the UPMPARK model in which atmospheric boundary layer is taken into account, but they may not work for complicated topography [26]. The generalized actuator disk model, the actuator line model and the actuator surface model are another series of wake models [23,27].…”

Section: Virtual Particle Model For Turbine Wake Flowmentioning

confidence: 99%

Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction

et al. 2016

View full text Add to dashboard Cite

a b s t r a c tIn the optimization of wind turbine micro-siting of wind farms, the major target is to maximize the total energy yield. But considering from the aspect of the power grid, the sensitivity of wind power generation to varying incoming wind direction is also an essential factor. However, most existing optimization approaches on wind turbine micro-siting are focused on increasing the total power yield only. In this paper, by employing computational fluid dynamics and the virtual particle model for the simulation of turbine wake flow, a sensitivity index is proposed to quantitatively evaluate the variation of power generation under varying wind direction. Typical turbine layouts obtained by existing power optimization approaches are evaluated for stability. Results indicate that regularly arranged turbine layouts are not suitable for stable power production. Based on solutions from the power optimization, a secondstage optimization using Particle Swarm Optimization algorithm is presented. The proposed optimization method adjusts the positions of the turbines locally, aiming at increasing the stability of wind farm power generation without damaging its advantage of high power yield. Case studies on flat terrain and complex terrain both demonstrate the effectiveness of the present local adjustment optimization method.

show abstract

Survey of modelling methods for wind turbine wakes and wind farms

Cited by 414 publications

References 25 publications

Optimization of wind plant layouts using an adjoint approach

Optimization of wind plant layouts using an adjoint approach

Progress and recent trends in wind energy

Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction

Contact Info

Product

Resources

About