Articles | Volume 2, issue 1
https://doi.org/10.5194/wes-2-175-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/wes-2-175-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
28 Mar 2017
Research article |
| 28 Mar 2017
Monitoring offshore wind farm power performance with SCADA data and an advanced wake model
Niko Mittelmeier
CORRESPONDING AUTHOR
Senvion GmbH, Überseering 10, 22297 Hamburg, Germany
Tomas Blodau
Senvion GmbH, Überseering 10, 22297 Hamburg, Germany
Martin Kühn
ForWind – Carl von Ossietzky University of Oldenburg, Institute of
Physics, Küpkersweg 70, 26129 Oldenburg Germany
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Cited
12 citations as recorded by crossref.
- Wake steering optimization under uncertainty J. Quick et al. 10.5194/wes-5-413-2020
- Joint state-parameter estimation for a control-oriented LES wind farm model B. Doekemeijer et al. 10.1088/1742-6596/1037/3/032013
- Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control B. Doekemeijer et al. 10.5194/wes-3-749-2018
- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
- An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects N. Mittelmeier et al. 10.5194/wes-2-477-2017
- The Financial Benefits of Various Catastrophic Failure Prevention Strategies in a Wind Farm: Two market studies (UK-Spain) N. Yürüşen et al. 10.1088/1742-6596/926/1/012014
- Statistical Evaluation of SCADA data for Wind Turbine Condition Monitoring and Farm Assessment E. Gonzalez et al. 10.1088/1742-6596/1037/3/032038
- Effects of the pre-processing algorithms in fault diagnosis of wind turbines P. Marti-Puig et al. 10.1016/j.envsoft.2018.05.002
- Wind Energy Assessment for Renewable Energy Communities S. Araveti et al. 10.3390/wind2020018
- Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. 10.3390/en15061964
- On the impact of layout in the dynamics of wind turbine arrays under passive oscillations Y. Jin et al. 10.1063/5.0095420
- From SCADA to lifetime assessment and performance optimization: how to use models and machine learning to extract useful insights from limited data N. Dimitrov & A. Natarajan 10.1088/1742-6596/1222/1/012032
11 citations as recorded by crossref.
- Wake steering optimization under uncertainty J. Quick et al. 10.5194/wes-5-413-2020
- Joint state-parameter estimation for a control-oriented LES wind farm model B. Doekemeijer et al. 10.1088/1742-6596/1037/3/032013
- Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control B. Doekemeijer et al. 10.5194/wes-3-749-2018
- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
- An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects N. Mittelmeier et al. 10.5194/wes-2-477-2017
- The Financial Benefits of Various Catastrophic Failure Prevention Strategies in a Wind Farm: Two market studies (UK-Spain) N. Yürüşen et al. 10.1088/1742-6596/926/1/012014
- Statistical Evaluation of SCADA data for Wind Turbine Condition Monitoring and Farm Assessment E. Gonzalez et al. 10.1088/1742-6596/1037/3/032038
- Effects of the pre-processing algorithms in fault diagnosis of wind turbines P. Marti-Puig et al. 10.1016/j.envsoft.2018.05.002
- Wind Energy Assessment for Renewable Energy Communities S. Araveti et al. 10.3390/wind2020018
- Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. 10.3390/en15061964
- On the impact of layout in the dynamics of wind turbine arrays under passive oscillations Y. Jin et al. 10.1063/5.0095420
Latest update: 18 Apr 2024
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...
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