Journal cover Journal topic
Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
Wind Energ. Sci., 2, 175-187, 2017
http://www.wind-energ-sci.net/2/175/2017/
doi:10.5194/wes-2-175-2017
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research articles
28 Mar 2017
Monitoring offshore wind farm power performance with SCADA data and an advanced wake model
Niko Mittelmeier1, Tomas Blodau1, and Martin Kühn2 1Senvion GmbH, Überseering 10, 22297 Hamburg, Germany
2ForWind – Carl von Ossietzky University of Oldenburg, Institute of Physics, Küpkersweg 70, 26129 Oldenburg Germany
Abstract. Wind farm underperformance can lead to significant losses in revenues. The efficient detection of wind turbines operating below their expected power output and immediate corrections help maximize asset value. The method, presented in this paper, estimates the environmental conditions from turbine states and uses pre-calculated lookup tables from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output ratio between two turbines are an indication of underperformance. The confidence of detected underperformance is estimated by a detailed analysis of the uncertainties of the method. Power normalization with reference turbines and averaging several measures performed by devices of the same type can reduce uncertainties for estimating the expected power. A demonstration of the method's ability to detect underperformance in the form of degradation and curtailment is given. An underperformance of 8 % could be detected in a triple-wake condition.

Citation: Mittelmeier, N., Blodau, T., and Kühn, M.: Monitoring offshore wind farm power performance with SCADA data and an advanced wake model, Wind Energ. Sci., 2, 175-187, doi:10.5194/wes-2-175-2017, 2017.
Publications Copernicus
Download
Short summary
Efficient detection of wind turbines operating below their expected power output and immediate corrections help maximize asset value. The method presented estimates the environmental conditions from turbine states and uses pre-calculated power lookup tables from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output are an indication of underperformance. A demonstration of the method's ability to detect underperformance is given.
Efficient detection of wind turbines operating below their expected power output and immediate...
Share