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  4. Modern Strategies for Controlling Wind Power Plants: Technologies, Challenges and Prospects

Modern Strategies for Controlling Wind Power Plants: Technologies, Challenges and Prospects

JEECS.
2024;
Volume 10, Number 1
: pp. 56 – 63
https://doi.org/10.23939/jeecs2024.01.056
Received: April 20, 2024
Revised: June 14, 2024
Accepted: June 21, 2024

N. Kurylko, R. Fedoryshyn. Modern strategies for controlling wind power plants: technologies, challenges and prospects. Energy Engineering and Control Systems, 2024, Vol. 10, No. 1, pp. 56 – 63. https://doi.org/10.23939/jeecs2024.01.056

Authors:
  1. Nazarii Kurylko
  2. Roman Fedoryshyn
1
Lviv Polytechnic National University
2
Lviv Polytechnic National University

This paper explores the evolution of wind power plant (WPP) control strategies, from simple approaches aimed at optimizing the operation of individual wind turbines to the development of more complex systems that manage WPPs as single integrated entities. Particular attention is paid to the key requirements for WPP control systems and the analysis of WPP structure, especially in the context of their integration into the overall power system. The main objectives of WPP control systems have been studied. The paper presents a detailed review and analysis of the control strategies that are being actively investigated. The control strategies that have successfully found commercial application are identified, and directions for further research needed to optimize and improve these strategies are outlined.

wind power plant (WPP)
wind turbine
control system
wind power
control strategy
  1. World Population Review. Wind Power by Country 2024: https://worldpopulationreview.com/country-rankings/wind-power-by-country. (accessed on June 9, 2024)
  2. Laffitte, T., & Moshenets, I. (2023). Synchronized: The impact of the war on Ukraine’s energy landscape. Foreign Policy Research Institute. https://www.fpri.org/wp-content/uploads/2023/12/ukraine-energy-landscape.pdf. (accessed on June 9, 2024)
  3. National Renewable Energy Laboratory. (2023). Ukraine fights to build a more resilient, renewable energy system in the midst of war. NREL. Retrieved from https://www.nrel.gov/news/features/2023/ukraine-fights-to-build-a-more-resilient-renewable-energy-system-in-the-midst-of-war.html. (accessed on June 9, 2024)
  4. Wiser, R., Rand, J., Seel, J., Beiter, P., Baker, E., Lantz, E., & Gilman, P. (2021). Expert elicitation survey predicts 37% to 49% declines in wind energy costs by 2050. Nature Energy, 6(5), 555-565. https://doi.org/10.1038/s41560-021-00810-z.
  5. Wright AD, Fingersh LJ. Advanced Control Design for Wind Turbines: Part I: Control Design, Implementation, and Initial Tests. Prepared under Task No. WER8.2101. Technical Report. NREL/TP-500-42437; March 2008. Available from: https://www.nrel.gov/docs/fy08osti/42437.pdf. (accessed on June 9, 2024)
  6. Schlueter, A., Javid, M., Sørensen, P., Kristoffersen, J. R., & Christiansen, C. (2022). Wind farm flow control: prospects and challenges. Wind Energy Science, 7(2271), 1-15. https://doi.org/10.5194/wes-7-2271-2022.
  7. Altin, M., Teodorescu, R., Bak-Jensen, B., Rodriguez, P., & Kjær, P. C. (2010). Aspects of wind power plant collector network layout and control architecture. In Proceedings of the Danish PhD Seminar on Detailed Modelling and Validation of Electrical Components and Systems 2010 (pp. 46-52). Fredericia, Denmark: Energinet.dk.
  8. Altın, M., Göksu, Ö., Teodorescu, R., Rodriguez, P., Jensen, B.-B., & Helle, L. (2010). Overview of recent grid codes for wind power integration. In Proceedings of the 12th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM 2010). IEEE. https://doi.org/10.1109/OPTIM.2010.5510521.
  9. Tsili, M., & Papathanassiou, S. (2009). Review of grid code technical requirements for wind farms. IET Renewable Power Generation, 3(3), 308-332. https://doi.org/10.1049/iet-rpg.2008.0070.
  10. International Electrotechnical Commission. IEC 61400-21: Measurement and assessment of power quality characteristics of grid connected wind turbines. Available from: https://webstore.iec.ch/publication/2604. (accessed on June 9, 2024)
  11. Institute of Electrical and Electronics Engineers. IEEE Std 1547-2018, IEEE Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces. Available from: https://standards.ieee.org/standard/1547-2018.html. (accessed on June 9, 2024)
  12. Sourkounis C, Tourou P. Grid Code Requirements for Wind Power Integration in Europe. Conference Papers in Science. 2013;2013(1). https://doi.org/10.1155/2013/437674.
  13. Díaz-González, F., Hau, M., Sumper, A., & Gomis-Bellmunt, O. (2014). Participation of wind power plants in system frequency control: Review of grid code requirements and control methods. Renewable and Sustainable Energy Reviews, 34, 551-564. https://doi.org/10.1016/j.rser.2014.03.040.
  14. Etxegarai, A., Eguia, P., Torres, E., Buigues, G., & Iturregi, A. (2017). Current procedures and practices on grid code compliance verification of renewable power generation. Renewable and Sustainable Energy Reviews, 71, 191-202. https://doi.org/10.1016/j.rser.2016.12.051.
  15. Bossanyi, E. A. (2003). Individual blade pitch control for load reduction. Wind Energy, 6(2), 119-128. https://doi.org/10.1002/we.76.
  16. Ossmann D, Seiler P, Milliren C, Danker A. Field testing of multi-variable individual pitch control on a utility-scale wind turbine. Renewable Energy. 2021;170(2). https://doi.org/10.1016/j.renene.2021.02.039.
  17. Jiménez A, Crespo A, Migoya E. Application of a LES technique to characterize the wake deflection of a wind turbine in yaw. Wind Energy. 2010;13:559-572. https://doi.org/10.1002/we.380.
  18. Gebraad PMO, Teeuwisse FW, van Wingerden JW, et al. Wind plant power optimization through yaw control using a parametric model for wake effects – a CFD simulation study. Wind Energy. 2016;19:95-114. https://doi.org/10.1002/we.1822.
  19. Campagnolo F, Petrović V, Schreiber J, et al. Wind tunnel testing of a closed-loop wake deflection controller for wind farm power maximization. J Phys: Conf Ser. 2016;753:032006. https://doi.org/10.1002/we.1822.
  20. Siemens Gamesa Renewable Energy. (n.d.). WakeAdapt: Optimizing Wind Farm Performance. Retrieved from https://www.siemensgamesa.com/products-and-services/services/wind-services/wake-adapt (accessed on June 9, 2024)
  21. WindESCo. (n.d.). Swarm™: Wind Farm Control. Retrieved from https://www.windesco.com/swarm (accessed on June 9, 2024)
  22. Scholbrock, A., Fleming, P., Schlipf, D., Wright, A., Johnson, K., & Wang, N. (2016). Lidar-enhanced wind turbine control: Past, present, and future. In 2016 American Control Conference (ACC) (pp. Date). IEEE. https://doi.org/10.1109/ACC.2016.7525113
  23. Schlipf D, Schlipf DJ, Kühn M. Nonlinear model predictive control of wind turbines using LIDAR. Wind Energy. 2012;16(7):1107-1129. https://doi.org/10.1002/we.1533
  24. Raach S, Schlipf D, Cheng PW. Lidar-based wake tracking for closed-loop wind farm control. Wind Energ Sci. 2017;2:257-267. https://doi.org/10.5194/wes-2-257-2017
  25. Windar Photonics. Windeye. Available at: https://www.windarphotonics.com/windeye. (accessed on June 9, 2024)
  26. Sun, Y., Tang, X., Sun, X., Jia, D., Cao, Z., Pan, J., & Xu, B. (2018). Model predictive control and improved low-pass filtering strategies based on wind power fluctuation mitigation. Journal of Modern Power Systems and Clean Energy, 6(5), https://doi.org/10.1007/s40565-018-0474-5.
  27. Yang X, Maciejowski JM. Fault-tolerant model predictive control of a wind turbine benchmark. IFAC Proceedings Volumes. 2012;45(20):337-342. https://doi.org/10.3182/20120829-3-MX-2028.00134
  28. Cañizo, M., Onieva, E., Conde, A., Charramendieta, S., & Trujillo, S. (2017). Real-time predictive maintenance for wind turbines using Big Data frameworks. Proceedings of the 2017 IEEE International Conference on Prognostics and Health Management, 70-77. https://doi.org/10.1109/ICPHM.2017.7998308.
  29. Turnbull, A., & Carroll, J. (2021). Cost benefit of implementing advanced monitoring and predictive maintenance strategies for offshore wind farms. Energies, 14(16), 4922. https://doi.org/10.3390/en14164922.