Journal cover Journal topic
Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
Journal topic
Volume 2, issue 1
Wind Energ. Sci., 2, 175-187, 2017
https://doi.org/10.5194/wes-2-175-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Wind Energ. Sci., 2, 175-187, 2017
https://doi.org/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

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 Niko Mittelmeier et al.
  • 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.

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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...
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