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Delegates are invited to meet and discuss with the poster presenters in this topic directly after the session 'Wakes: Do we need different models for onshore and offshore wind farms?' taking place on Wednesday, 12 March 2014 at 16:30 -18:00. The meet-the-authors will take place in the poster area.

Tuhfe Gocmen Bozkurt Technical University of Denmark, Denmark
Tuhfe Gocmen Bozkurt (1) F P Gregor Giebel (1) Niels Kjølstad Poulsen (1) Pierre-Elouan Réthoré (1) Mahmood Mirzaei (1)
(1) Technical University of Denmark, Roskilde, Denmark

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Wind speed estimation and parametrization of wake models for downregulated offshore wind farms


Currently, the Transmission System Operators (TSOs) have no real way to determine exactly the available power of a down-regulated wind farm. The PossPOW project is addressing that need (see and the poster also in the technical track).
In this paper, the main challenges encountered in the PossPOW project, namely the effective wind speed estimation and re-parametrization of wake models for downregulation periods in large offshore wind farms will be explained and the proposed solutions together with the obtained results are presented.


Even though modern wind turbines have a SCADA signal called possible power, the sum of those powers is more than the available power of the whole wind farm since the turbines downwind of downregulated turbine(s) see more wind that would be there without the regulation, due to decreased wake effects. Therefore, models from various disciplines, including wake modeling of large offshore wind farms, aerodynamic models for wind turbines, stochastic model estimation and computer simulations have to be considered in order to estimate the available power of an offshore wind farm.

Main body of abstract

The estimation of the available power of the down-regulated wind farm starts with the calculation of rotor wind speeds of the turbines using the power, pitch angle and rotational speed measurements as inputs which also requires the power curve information. Since during downregulation periods, the power curve provided for optimum operational conditions is no longer valid, the power coefficient needs to be updated for changing pitch angle values which is the key aerodynamic parameter for down-regulation. Considering the generic structure of the project, an algebraic expression for power coefficient is used and the wind speed is calculated for each turbine iteratively. Using second-wise data obtained from the Horns Rev I wind farm, a good agreement with both the power curve wind speeds and the nacelle anemometer measurements is achieved.
To consider the changing wake effects for normal and downregulated operations, the rotor wind speed values of upstream turbines are to be taken as inputs to the wake model as they are not affected by the wake (downregulated or not). Then we apply the model directly to estimate the velocity deficit and calculate the possible power output of the wind farm. However, most existing wake models have only been used to acquire long term, statistical information and verified using 10-min averaged data. Therefore, re-parameterization of wake models will be performed such that the parameters in the model such as wake expansion and “sweeping” speed will be calibrated for different averaging time scales using second-wise data obtained from Horns Rev I.


The intermediate results regarding the wind speed estimation and parametrization of wake models for downregulated offshore wind farms in the context of the PossPOW project are to be presented. The developed technology will later be tested and verified on some of the large offshore wind farms owned by project partners.

Learning objectives
In this paper, the requirements of the available power estimation process in terms of wake modelling is underlined.
The validity of the time averaging intervals of the existing wake models for that specific process is questioned.
The estimation of rotor effective wind speed and re-parametrization of the wake modelling is described.
The existing wake models are tuned to reproduce the transient effects more effectively using high frequency data set.