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First Approximation of Application of Federated Learning to Wind Turbines

A. Gil Macia(1), J. Enrique Sierra-García(2) and Matilde Santos(3)

1. Department of Computer Science and Automatic Control, UNED, Madrid (Spain)
2. Department of Digitalization, University of Burgos, (Spain)
3. Institute of Knowledge Technology, Complutense University of Madrid, (Spain)

2026-02-15

This work investigates the application of FederatedLearning techniques to reduce the training time of wind turbine power stabilization controllers on a wind farm. A Reinforcement Learning controller based on Q-learning is implemented and the results of the individual controller are compared with a system of 4 wind turbines using Federated Learning. The simulation results show how this technique significantly improves the convergence time of the controller when compared to control strategies without federated learning. The preliminary results demonstrate how Federated Learning has great potential for improving the effectiveness of wind turbine controllers while maintaining the privacy and security of their operational data.

Key words: Q-Learning, Federated Learning, Intelligent Control, Reinforcement Learning, Wind Turbine.

[1] Hassan, Q., Viktor, P., Al-Musawi, T. J., Ali, B. M., Algburi, S., Alzoubi, H. M., ... & Jaszczur, M. (2024). The renewable energy role in the global energy Transformations. Renewable Energy Focus, 48, 100545.

[2] Zhou, Bowen & Zhang, Zhibo & Li, Guangdi & Yang, Dongsheng & Santos, Matilde. (2023). Review of Key Technologies for Offshore Floating Wind Power Generation. Energies. 16. 710. 10.3390/en16020710.

[3] Pandit, R., Infield, D., & Santos, M. (2022). Accounting for environmental conditions in data-driven wind turbine power models. IEEE Transactions on Sustainable Energy, 14(1), 168-177.

[4] Xie, J., Dong, H., & Zhao, X. (2023). Power regulation and load mitigation of floating wind turbines via reinforcement learning. IEEE Transactions on Automation Science and Engineering.

[5] Dong, H., Xie, J., & Zhao, X. (2022). Wind farm control technologies: from classical control to reinforcement learning. Progress in Energy, 4(3), 032006.

[6] Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216, 106775.

[7] Sierra-García, J. E., & Santos, M. (2024). Federated discrete reinforcement learning for automatic guided vehicle control. Future Generation Computer Systems, 150, 78-89. https://doi.org/10.1016/j.future.2023.08.021.

[8] McMahan, H.B., Moore, E., Ramage, D., Hampson, S., & Arcas, B.A. (2016). Communication-Efficient Learning of Deep Networks from Decentralized Data. International Conference on Artificial Intelligence and Statistics.

[9] Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., & Zhang, W. (2023). A survey on federated learning: challenges and applications. International Journal of Machine Learning and Cybernetics, 14(2), 513-535.

[10] Grataloup, A., Jonas, S., & Meyer, A. (2024). A review of federated learning in renewable energy applications: Potential, challenges, and future directions. Energy and AI, 100375.

[11] Weber, J., Gurtner, M., Lobe, A., Trachte, A., & Kugi, A. (2024). Combining federated learning and control: A survey. IET Control Theory & Applications, 18(18), 2503-2523.

[12] Arooj, Q. (2024). FedWindT: Federated learning assisted transformer architecture for collaborative and secure wind power forecasting in diverse conditions. Energy, 309, 133072.

[13] Alshardan, A., Tariq, S., Bashir, R. N., Saidani, O., & Jahangir, R. (2024). Federated learning (FL) model of wind power prediction. IEEE Access.

[14] Soler, D., Mariño, O., Huergo, D., de Frutos, M., & Ferrer, E. (2024). Reinforcement learning to maximize wind turbine energy generation. Expert Systems with Applications, 249, 123502.

[15] Sierra-García, J. E., & Santos, M. (2020). Exploring reward strategies for wind turbine pitch control by reinforcement learning. Applied Sciences, 10(21), 7462.

[16] Amiri, Roohollah & Mehrpouyan, Hani & Fridman, Lex & Mallik, Ranjan & Nallanathan, Arumugam & Matolak, David. (2018). A Machine Learning Approach for Power Allocation in HetNets Considering QoS. 10.48550/arXiv.1803.06760.

[17] Watkins, Christopher & Dayan, Peter. (1992). Technical Note: Q-Learning. Machine Learning. 8. 279-292. 10.1007/BF00992698.

[18] T. Sun, D. Li and B. Wang, "Decentralized Federated Averaging," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 4, pp. 4289-4301, 1 April 2023, doi: 10.1109/TPAMI.2022.3196503.

[19] The MathWorks, Inc. “Control design for wind turbine.” mathworks.com. Accessed: February 12, 2025. [Online]. Available: https://nl.mathworks.com/help/control/ug/wind-turbine-control-design.html