Articles | Volume 4, issue 2
Wind Energ. Sci., 4, 355–368, 2019
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.
Wind Energ. Sci., 4, 355–368, 2019
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
Wind direction estimation using SCADA data with consensus-based optimization
Jennifer Annoni et al.
Viewed
Total article views: 4,339 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Oct 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,112 | 1,143 | 84 | 4,339 | 95 | 72 |
- HTML: 3,112
- PDF: 1,143
- XML: 84
- Total: 4,339
- BibTeX: 95
- EndNote: 72
Total article views: 3,172 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 20 Jun 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,478 | 619 | 75 | 3,172 | 79 | 60 |
- HTML: 2,478
- PDF: 619
- XML: 75
- Total: 3,172
- BibTeX: 79
- EndNote: 60
Total article views: 1,167 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Oct 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 634 | 524 | 9 | 1,167 | 16 | 12 |
- HTML: 634
- PDF: 524
- XML: 9
- Total: 1,167
- BibTeX: 16
- EndNote: 12
Viewed (geographical distribution)
Total article views: 3,069 (including HTML, PDF, and XML)
Thereof 2,967 with geography defined
and 102 with unknown origin.
Total article views: 2,180 (including HTML, PDF, and XML)
Thereof 2,082 with geography defined
and 98 with unknown origin.
Total article views: 889 (including HTML, PDF, and XML)
Thereof 885 with geography defined
and 4 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
17 citations as recorded by crossref.
- Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2 P. Fleming et al. 10.5194/wes-5-945-2020
- Synthesis and characterization of aluminum coated Carica papaya extracts M. Emetere & I. Ahiara 10.1016/j.cdc.2020.100381
- Decentralised optimisation for large offshore wind farms using a sparsified wake directed graph T. Shu et al. 10.1016/j.apenergy.2021.117986
- Observability of the ambient conditions in model‐based estimation for wind farm control: A focus on static models B. Doekemeijer & J. van Wingerden 10.1002/we.2495
- Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis I. Eguinoa et al. 10.1002/adc2.80
- H∞ network optimization for edge consensus O. Farhat et al. 10.1016/j.ejcon.2021.06.032
- 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
- Analytical model for the power–yaw sensitivity of wind turbines operating in full wake J. Liew et al. 10.5194/wes-5-427-2020
- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
- Grand challenges in the science of wind energy P. Veers et al. 10.1126/science.aau2027
- Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020
- 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
- IEA Wind Task 32 and Task 37: Optimizing Wind Turbines with Lidar-Assisted Control Using Systems Engineering E. Simley et al. 10.1088/1742-6596/1618/4/042029
- Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions B. Doekemeijer et al. 10.1016/j.renene.2020.04.007
- 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
- Wind Farm Modeling with Interpretable Physics-Informed Machine Learning M. Howland & J. Dabiri 10.3390/en12142716
- Grand challenges in the science of wind energy P. Veers et al. 10.1126/science.aau2027
16 citations as recorded by crossref.
- Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2 P. Fleming et al. 10.5194/wes-5-945-2020
- Synthesis and characterization of aluminum coated Carica papaya extracts M. Emetere & I. Ahiara 10.1016/j.cdc.2020.100381
- Decentralised optimisation for large offshore wind farms using a sparsified wake directed graph T. Shu et al. 10.1016/j.apenergy.2021.117986
- Observability of the ambient conditions in model‐based estimation for wind farm control: A focus on static models B. Doekemeijer & J. van Wingerden 10.1002/we.2495
- Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis I. Eguinoa et al. 10.1002/adc2.80
- H∞ network optimization for edge consensus O. Farhat et al. 10.1016/j.ejcon.2021.06.032
- 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
- Analytical model for the power–yaw sensitivity of wind turbines operating in full wake J. Liew et al. 10.5194/wes-5-427-2020
- Wind farm control ‐ Part I: A review on control system concepts and structures L. Andersson et al. 10.1049/rpg2.12160
- Grand challenges in the science of wind energy P. Veers et al. 10.1126/science.aau2027
- Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020
- 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
- IEA Wind Task 32 and Task 37: Optimizing Wind Turbines with Lidar-Assisted Control Using Systems Engineering E. Simley et al. 10.1088/1742-6596/1618/4/042029
- Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions B. Doekemeijer et al. 10.1016/j.renene.2020.04.007
- 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
- Wind Farm Modeling with Interpretable Physics-Informed Machine Learning M. Howland & J. Dabiri 10.3390/en12142716
1 citations as recorded by crossref.
Latest update: 24 Jan 2022
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...
