Journal cover Journal topic
Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
Wind Energ. Sci., 2, 477-490, 2017
https://doi.org/10.5194/wes-2-477-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research articles
18 Oct 2017
An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects
Niko Mittelmeier1, Julian Allin1, Tomas Blodau1, Davide Trabucchi2, Gerald Steinfeld2, Andreas Rott2, and Martin Kühn2 1Senvion GmbH, Überseering 10, 22297 Hamburg, Germany
2ForWind – University of Oldenburg, Institute of Physics, Küpkersweg 70, 26129 Oldenburg, Germany
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.

Citation: Mittelmeier, N., Allin, J., Blodau, T., Trabucchi, D., Steinfeld, G., Rott, A., and Kühn, M.: An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects, Wind Energ. Sci., 2, 477-490, https://doi.org/10.5194/wes-2-477-2017, 2017.
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Short summary
Stability classification is usually based on measurements from met masts, buoys or lidars. The objective of this paper is to find a classification for stability based on wind turbine supervisory control and data acquisition measurements in order to fit engineering wake models better to the current ambient conditions. The proposed signal is very sensitive to increased turbulence. It allows us to distinguish between conditions with different magnitudes of wake effects.
Stability classification is usually based on measurements from met masts, buoys or lidars. The...
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