Journal cover Journal topic
Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
Journal topic
Volume 4, issue 2
Wind Energ. Sci., 4, 355–368, 2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Wind Energ. Sci., 4, 355–368, 2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research articles 20 Jun 2019

Research articles | 20 Jun 2019

Wind direction estimation using SCADA data with consensus-based optimization

Jennifer Annoni et al.
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The aerodynamics of the curled wake: a simplified model in view of flow control
Luis A. Martínez-Tossas, Jennifer Annoni, Paul A. Fleming, and Matthew J. Churchfield
Wind Energ. Sci., 4, 127–138,,, 2019
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Analysis of control-oriented wake modeling tools using lidar field results
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Related subject area  
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Wind Energ. Sci., 4, 273–285,,, 2019
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Wind Energ. Sci., 4, 139–161,,, 2019
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Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control
Bart M. Doekemeijer, Sjoerd Boersma, Lucy Y. Pao, Torben Knudsen, and Jan-Willem van Wingerden
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Cited articles  
Baros, S. and Ilic, M.: Distributed Torque Control of Deloaded Wind DFIGs for Wind Farm Power Output Regulation, IEEE T. Power Syst., 32, 4590–4599, 2017.
Barthelmie, R. J., Wang, H., Doubrawa, P., and Pryor, S.: Best Practice for Measuring Wind Speeds and Turbulence Offshore through In-Situ and Remote Sensing Technologies, available at: (last access: June 2019), 2016.
Bay, C., Annoni, J., Taylor, T., Pao, L., and Johnson, K.: Active Power Control for Wind Farms Using Distributed Model Predictive Control and Nearest Neighbor Communication, in: IEEE 2018 Annual American Control Conference (ACC), 682–687, 2018.
Boyd, S., Parikh, N., Chu, E., Peleato, B., and Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers, Foundations and Trends® in Machine Learning, 3, 1–122, 2011.
Ebegbulem, J. and Guay, M.: Distributed Extremum Seeking Control for Wind Farm Power Maximization, in: International Federation of Automatic Control, IFAC-PapersOnLine, 50, 147–152, 2017.
Publications Copernicus
Short summary
Typically, turbines do not share information with nearby turbines in a wind farm. Relying on a single turbine sensor on the back of a turbine nacelle can lead to large errors in yaw misalignment or excessive yawing due to noisy sensor measurements. The wind farm consensus control approach in this paper shows the benefits of sharing information between nearby turbines by computing a robust estimate of the wind direction using noisy sensor information from these neighboring turbines.
Typically, turbines do not share information with nearby turbines in a wind farm. Relying on a...