An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects

Abstract

Abstract. For offshore wind farms, wake effects are among the largest sources of losses in energy production. At the same time, wake modelling is still associated with very high uncertainties. Therefore current research focusses on improving wake model predictions. It is known that atmospheric conditions, especially atmospheric stability, crucially influence the magnitude of those wake effects. The classification of atmospheric stability is usually based on measurements from met masts, buoys or lidar (light detection and ranging). In offshore conditions these measurements are expensive and scarce. However, every wind farm permanently produces SCADA (supervisory control and data acquisition) measurements. The objective of this study is to establish a classification for the magnitude of wake effects based on SCADA data. This delivers a basis to fit engineering wake models better to the ambient conditions in an offshore wind farm. The method is established with data from two offshore wind farms which each have a met mast nearby. A correlation is established between the stability classification from the met mast and signals within the SCADA data from the wind farm. The significance of these new signals on power production is demonstrated with data from two wind farms with met mast and long-range lidar measurements. Additionally, the method is validated with data from another wind farm without a met mast. The proposed signal consists of a good correlation between the standard deviation of active power divided by the average power of wind turbines in free flow with the ambient turbulence intensity (TI) when the wind turbines were operating in partial load. It allows us to distinguish between conditions with different magnitudes of wake effects. The proposed signal is very sensitive to increased turbulence induced by neighbouring turbines and wind farms, even at a distance of more than 38 rotor diameters.

Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

“…In some studies, it is also expressed more specifically as a function of the particular conditions at the wind farm, using, for example, the roughness length and the atmospheric stability (Peña and Rathmann, 2014) or the ambient turbulence intensity (Peña et al, 2015;Thorgersen et al, 2011). The link between wake growth and TI was pointed out by Lissaman (1976), but more recent studies, based on wind tunnel, large eddy simulations (LESs), Reynolds-averaged Navier-Stokes (RANS) simulations and full-scale turbine data, clearly identify TI as one of the most influencing parameters on the wake growth and magnitude of the wake deficits (Bastankhah and Porté-Agel, 2014;Mittelmeier et al, 2017;Santhanagopalan et al, 2018;Annoni et al, 2018).…”

“…(1), there is only one parameter to be tuned in the Jensen model: the wake decay constant. This empirical constant is supposed to vary from one wind farm to another but generally the two recommended values of 0.075 and 0.05 are used for onshore and offshore wind farms, respectively (Mortensen et al, 2011). In some studies, it is also expressed more specifically as a function of the particular conditions at the wind farm, using, for example, the roughness length and the atmospheric stability (Peña and Rathmann, 2014) or the ambient turbulence intensity (Peña et al, 2015;Thorgersen et al, 2011).…”

Robust active wake control in consideration of wind direction variability and uncertainty

“…In addition, there are inaccuracies in the determination of the wind direction and the alignment of the turbine. Such types of measurement errors are commonly assumed to be independent and normally distributed (Murcia et al, 2015). For a random variable that is based on two or more independent distributions, its distribution can be determined by the convolution of the individual distributions.…”

Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

“…In some studies it is also expressed more specifically as a function of the particular conditions at the wind farm, using for example the roughness length and the atmospheric stability (Peña and Rathmann, 2014) or the ambient turbulence intensity (Peña et al, 2015;Thorgersen et al, 2011). Recent studies, based on wind tunnel, Large Eddy Simulations (LES) and full scale turbine data, clearly identifies TI as one of the most influencing parameter on the wake growth and magnitude of the wake deficits (Bastankhah and Porté-Agel, 2014;Mittelmeier et al, 2017;Annoni et al, 2018).…”