Online Modeling of Wind Farm Power for Performance Surveillance and Optimization

Trujillo, Juan José; Wessel, Arne; Waldl, I.; Lange, Bernhard

doi:10.1007/978-3-540-33866-6_29

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…1,2 A large and still growing body of literature has dealt with models of wind farms with either the purpose of farm layout optimization [3][4][5] or power quality improvement. [6][7][8][9] Furthermore, several attempts have been made in modeling of the flow in the wind farms. An example of these is the study carried out by Crespo et al 10 A well-known modeling approach, addressed by Crespo et al, 10 assumes that when a field contains a large number of wind turbines, the turbines are considered as distributed roughness elements.…”

Section: Introductionmentioning

confidence: 99%

State‐space representation of the wind flow model in wind farms

2013

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A dynamic model for the wind flow in wind farms is developed in this paper. The model is based on the spatial discretization of the linearized Navier-Stokes equation combined with the vortex cylinder theory. The spatial discretization of the model is performed using the finite difference method, which provides the state-space form of the dynamic wind farm model. The model provides an approximation of the behavior of the flow in the wind farm and obtains the wind speed in the vicinity of each wind turbine. Afterwards, the model is validated using measurement data of Energy research Center of the Netherlands' Wind turbine Test site in Wieringermeer in the Netherlands and by employing the outcomes of two other wind flow models. The end goal of this work is to present the wind farm flow model by ordinary differential equations, to be applied in wind farm control algorithms along with load and power optimizations.

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Section: Introductionmentioning

confidence: 99%

State‐space representation of the wind flow model in wind farms

2013

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“…[1][2][3] More detailed results can be obtained by comparing each 10 min recording individually with model predictions. This way to analyse data is frequently used in operational analysis, 4,5 since it allows to include recordings with offl ine turbines. However, the individual analysis has not received a lot of attention in the evaluation of wake-fl ow models.…”

Section: Introductionmentioning

confidence: 99%

Model‐based analysis of wake‐flow data in the Nysted offshore wind farm

et al. 2008

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Wake-flow data of the Nysted offshore wind farm is analysed with the engineering wake model of Jensen. Two-parameter fits referring to wake-decay parameter and wind direction are made to each individual 10 min-averaged recording of turbine powers. The fitted wind direction neither agrees with the measurements at the meteorological masts nor the turbine orientations, but is in agreement with the power deficit ratios between second-and first-row turbines. The fitted wake-decay parameter varies roughly between k = 0.02 and 0.04, depending on the wind direction with respect to the wind farm geometry. Its average ≈ 0.03 is smaller than the current standard k = 0.04, indicating that offshore wakes might be more narrow. No significant dependence on flow stability is found. Compared to two-parameter fits, one-parameter fits with pre-assigned wind direction perform worse; they lead to ≈ 0.04 and tend to overpredict wind farm power.

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Online Modeling of Wind Farm Power for Performance Surveillance and Optimization

Cited by 2 publications

References 2 publications

State‐space representation of the wind flow model in wind farms

State‐space representation of the wind flow model in wind farms

Model‐based analysis of wake‐flow data in the Nysted offshore wind farm

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