Wind direction estimation using SCADA data with consensus-based optimization

Wind Energy Science

Volumn 4, Issue 2, 2019, Pages 355-368

  Annoni, Jennifer ^a   Bay, Christopher ^a,b,c   Johnson, Kathryn ^a,b   Dall’Anese, Emiliano ^c   Quon, Eliot ^a   Kemper, Travis ^a   Fleming, Paul ^a  

^a NATIONAL RENEWABLE ENERGY LABORATORY   (United States)

^b Department of Metallurgical and Materials Engineering   (United States)

^c University of Colorado at Boulder   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRIC UTILITIES; INFORMATION USE; ITERATIVE METHODS; WIND POWER; WIND TURBINES;

DIRECTION ESTIMATION; KEY ELEMENTS; LOCAL CONDITIONS; LOCAL SENSORS; OPTIMISATIONS; PERFORMANCE; REAL- TIME; SUPERVISORY CONTROL AND DATA ACQUISITION; WIND DIRECTIONS; WIND FARM;

DATA ACQUISITION;

EID: 85068504926     PISSN: 23667443     EISSN: 23667451     Source Type: Journal    
DOI: 10.5194/wes-4-355-2019     Document Type: Article

References (24)

1. 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.
2. 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: http://www.geo.cornell.edu/eas/PeoplePlaces/Faculty/spryor/DoE_AIATOWEA/DoE2016Barthelmieetal_BestPractice_ 070716-1djxj4x.pdf (last access: June 2019), 2016.
3. 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.
4. ® in Machine Learning, 3, 1–122, 2011.
5. 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.
6. Ferrari, S., Foderaro, G., Zhu, P., and Wettergren, T. A.: Distributed optimal control of multiscale dynamical systems: a tutorial, IEEE Contr. Syst. Mag., 36, 102–116, 2016.
7. Fleming, P., Annoni, J., Shah, J. J., Wang, L., Ananthan, S., Zhang, Z., Hutchings, K., Wang, P., Chen, W., and Chen, L.: Field test of wake steering at an offshore wind farm, Wind Energ. Sci., 2, 229–239, https://doi.org/10.5194/wes-2-229-2017, 2017.
8. Fleming, P. A., Gebraad, P. M., Lee, S., van Wingerden, J.-W., Johnson, K., Churchfield, M., Michalakes, J., Spalart, P., and Moriarty, P.: Evaluating techniques for redirecting turbine wakes using SOWFA, Renew. Energ., 70, 211–218, 2014a.
9. Fleming, P. A., Scholbrock, A. K., Jehu, A., Davoust, S., Osler, E., Wright, A. D., and Clifton, A.: Field-test results using a nacelle-mounted lidar for improving wind turbine power capture by reducing yaw misalignment, J. Phys. Conf. Ser., 524, 012002, https://doi.org/10.1088/1742-6596/524/1/012002, 2014b.
10. Gebraad, P., Teeuwisse, F., Wingerden, J., Fleming, P., Ruben, S., Marden, J., and Pao, L.: Wind plant power optimization through yaw control using a parametric model for wake effects—a CFD simulation study, Wind Energy, 19, 95–114, 2016.
11. Gionfra, N., Sandou, G., Siguerdidjane, H., Faille, D., and Loeven-bruck, P.: A Distributed Consensus Control Under Disturbances for Wind Farm Power Maximization, in: 2017 IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, Australia, 2015–2020, 2017.
12. Hallac, D., Leskovec, J., and Boyd, S.: Network lasso: Clustering and optimization in large graphs, in: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 387–396, 2015.
13. Lindenberg, S.: 20 % Wind Energy By 2030: Increasing Wind Energy’s Contribution to US Electricity Supply, EERE Publication and Product Library, Washington, D.C., USA, 2009.
14. Marden, J., Ruben, S., and Pao, L.: Surveying game theoretic approaches for wind farm optimization, in: 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, p. 1154, 2012.
15. Movric, K. H. and Lewis, F. L.: Cooperative optimal control for multi-agent systems on directed graph topologies, IEEE T. Au-tomat. Contr., 59, 769–774, 2014.
16. Peña, A., Hasager, C. B., Badger, M., Barthelmie, R. J., Bingöl, F., Cariou, J.-P., Emeis, S., Frandsen, S. T., Harris, M., Karagali, I., et al.: Remote sensing for wind energy, Risø National Laboratory for Sustainable Energy, Technical University, Risø, 2010.
17. Schlipf, D., Schlipf, D. J., and Kühn, M.: Nonlinear model predictive control of wind turbines using LIDAR, Wind Energy, 16, 1107–1129, 2013.
18. Scholbrock, A., Fleming, P., Fingersh, L., Wright, A., Schlipf, D., Haizmann, F., and Belen, F.: Field testing lidar-based feed-forward controls on the NREL controls advanced research turbine, in: 51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, p. 818, 2013.
19. Shamma, J.: Cooperative control of distributed multi-agent systems, Wiley Online Library, 2008.
20. Simley, E., Pao, L. Y., Frehlich, R., Jonkman, B., and Kelley, N.: Analysis of light detection and ranging wind speed measurements for wind turbine control, Wind Energy, 17, 413–433, 2014.
21. Soleimanzadeh, M., Wisniewski, R., and Johnson, K.: A distributed optimization framework for wind farms, J. Wind Eng. Ind. Aerod., 123, 88–98, 2013.
22. Spudić, V., Conte, C., Baotić, M., and Morari, M.: Cooperative distributed model predictive control for wind farms, Optim. Contr. Appl. Met., 36, 333–352, 2015.
23. Wang, L., Wen, J., Cai, M., and Zhang, Y.: Distributed Optimization Control Schemes Applied On Offshore Wind Farm Active Power Regulation, Energy Procedia, 105, 1192–1198, 2017.
24. Zhu, M. and Martínez, S.: Distributed optimization-based control of multi-agent networks in complex environments, Springer, 2015.