Articles | Volume 4, issue 2
https://doi.org/10.5194/wes-4-355-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-4-355-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jun 2019
Research article |
| 20 Jun 2019
| 20 Jun 2019
Wind direction estimation using SCADA data with consensus-based optimization
Jennifer Annoni
CORRESPONDING AUTHOR
National Renewable Energy Laboratory, Golden, CO, 80401 USA
Christopher Bay
National Renewable Energy Laboratory, Golden, CO, 80401 USA
Department of Electrical Engineering, Colorado School of Mines, Golden, CO 80401, USA
Department of Electrical, Computer, and Energy Engineering, University of Colorado – Boulder, Boulder, CO 80309, USA
Kathryn Johnson
National Renewable Energy Laboratory, Golden, CO, 80401 USA
Department of Electrical Engineering, Colorado School of Mines, Golden, CO 80401, USA
Emiliano Dall'Anese
Department of Electrical, Computer, and Energy Engineering, University of Colorado – Boulder, Boulder, CO 80309, USA
Eliot Quon
National Renewable Energy Laboratory, Golden, CO, 80401 USA
Travis Kemper
National Renewable Energy Laboratory, Golden, CO, 80401 USA
Paul Fleming
National Renewable Energy Laboratory, Golden, CO, 80401 USA
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Total article views: 4,472 (including HTML, PDF, and XML)
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- Power increases using wind direction spatial filtering for wind farm control: Evaluation using FLORIS, modified for dynamic settings M. Sinner et al. 10.1063/5.0039899
- Power prediction using high-resolution SCADA data with a farm-wide deep neural network approach S. Daenens et al. 10.1088/1742-6596/2767/9/092014
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- A dynamic model of wind turbine yaw for active farm control G. Starke et al. 10.1002/we.2884
- Are steady-state wake models and lookup tables sufficient to design profitable wake steering strategies? A Large Eddy Simulation investigation M. Lejeune et al. 10.1088/1742-6596/2767/9/092075
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- Decentralised optimisation for large offshore wind farms using a sparsified wake directed graph T. Shu et al. 10.1016/j.apenergy.2021.117986
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- A quantitative review of wind farm control with the objective of wind farm power maximization A. Kheirabadi & R. Nagamune 10.1016/j.jweia.2019.06.015
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- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
- Smart cooperative control scheme for large-scale wind farms based on a double-layer machine learning framework S. Yang et al. 10.1016/j.enconman.2023.116949
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- Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. 10.5194/wes-6-1427-2021
- Design of the American Wake Experiment (AWAKEN) field campaign M. Debnath et al. 10.1088/1742-6596/2265/2/022058
- Reconstruction of environmental site conditions by the integration of SCADA and reanalysis data A. Vad & C. Bottasso 10.1088/1742-6596/2767/9/092073
- Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-7-345-2022
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Latest update: 23 Nov 2024
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...
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