Hybrid Sliding Mode Control and RNN-Based Strategy for Maximizing Power Extraction in Small Wind Turbines for Electric Vehicles

Year 2025, Volume: 9 Issue: 1, 1 - 9, 31.03.2025

Khalid Alfuwail , Ali Nasir Md Shafiullah Ayman Abdallah

Abstract

Advanced technologies like Ram Air Turbines (RATs) are being investigated because of the aviation industry's need
for fuel-efficient and alternative renewable energy sources. In situations where power generation is necessary in the event of an
emergency involving unmanned aerial vehicles (UAVs), RATs are essential. Optimising the RATs' performance—including
power output and operational stability—under variable and unexpected wind conditions is the main obstacle, though.
Conventional control techniques frequently don't adjust to these changing conditions. In order to monitor the ideal turbine
rotation speed, a sliding mode control rule is developed in the proposed controller. This article emphasises the need of using a
recurrent neural network (RNN) to identify unpredictable wind turbine dynamics. Control over maximum power extraction is
then made possible by the development of an online update mechanism that provides real-time weight changes for the RNN.
Simulation findings show that, even in the presence of significant nonlinearities and system uncertainties, the proposed controller
performs 13 times better than a conventional control strategy in monitoring the ideal turbine rotation speed and obtaining the
maximum wind output from RATs.

Keywords

Wind Power, Maximum wind power extraction, UAVs, Sliding Mode Control, Recurrent Neural Network, Ram air unit air

Ethical Statement

The authors affirm that this research complied with ethical standards and professional integrity. No human or animal subjects were involved in this study, and all data used were obtained from publicly available sources or generated through simulations. Proper citations and acknowledgments have been provided for all referenced work to ensure transparency and academic honesty. This manuscript is the original work of the authors and has not been published or submitted elsewhere for publication. No conflicts of interest, financial or otherwise, are associated with this research. The study adheres to the ethical guidelines and policies of the journal to which it is submitted. Additionally, all authors contributed significantly to the research and have approved the final manuscript for submission.

Supporting Institution

KFUPM

Project Number

51

Thanks

IRC-SES

References

• ] G. Buticchi, P. Wheeler, and D. Boroyevich, “The more-electric aircraft and beyond,” Proceedings of the IEEE, vol. 111, no. 4, pp. 356–370,2023.
• [2] T. Moustafa and W. Moreno, “Ram air and wind energy harvesting survey for electrical vehicles and transportation,” in SoutheastCon 2017, 2017, pp. 1–7.
• [3] M. Hovanec, P. Korba, P. Šváb, R. Baláž, and M. Golisová, “Auxiliary power unit - system essentials,” in 2019 New Trends in Aviation Development (NTAD), 2019, pp. 73 76.
• [4] B. Yang, T. Yu, H. Shu, Y. Zhang, J. Chen, Y. Sang, and L. Jiang, “Passivity-based sliding-mode control design for optimal power extraction of a pmsg based variable speed wind turbine,” Renewable Energy, vol. 119, pp. 577–589, 2018. [Online].Available:https://www.sciencedirect.com/science/article/pii/S096014811731251X
• [5] B. Yang, T. Yu, H. Shu, J. Dong, and L. Jiang, “Robust sliding mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers,” Applied Energy, vol. 210, pp. 711–723, 2018. [Online].Available:https://www.sciencedirect.com/science/article/pii/S0306261917310486
• [6] S. Sangwian, “Multivariable sliding mode control design for aircraft engines,” Ph.D. dissertation, Cleveland State University, 2011.
• [7] X. Tianxiang, L. Yueliang, and Z. Dongyu, “Study on the rotation speed control method for ram air turbine system,” in CSAA/IET International Conference on Aircraft Utility Systems (AUS 2018). Institution of Engineering and Technology, 2018.
• [8] X. Yin, Z. Jiang, and L. Pan, “Recurrent neural network based adaptive integral sliding mode power maximization control for wind power systems,” Renewable Energy, vol. 145, pp. 1149–1157, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0960148 118315520
• [9] X. Zhao, L. Wang, and M. Zhou, “Comparative analysis of ram air turbine system for large civil aircraft,” in CSAA/IET International Conference on Aircraft Utility Systems (AUS 2022), vol. 2022. IET, 2022, pp. 429–434.
• [10] M. M. M. Saad, S. Mohd, M. F. Zulkafli, N. A. Samiran, and D. H. Didane, “Cfd simulation study on the performance of a modified ram air turbine (rat) for power generation in aircrafts,” Fluids, vol. 6, no. 11, 2021. [Online]. Available: https://www.mdpi.com/2311-5521/6/11/391
• [11] D. Jiménez-Santín, M. Cerrada, J. Enríquez-Zárate, D. Cabrera, and R.- V. Sánchez, “Drive-train third stage-based simplified dynamic modelling of a wind turbine oriented to vibration analysis,” in 2023 IEEE Seventh Ecuador Technical Chapters Meeting (ECTM), 2023, pp. 1–6.
• [12] Q. Lai, C. Shen, and D. Li, “Dynamic modeling and stability analysis for repeated lvrt process of wind turbine based on switched system theory,” 2024. [Online]. Available: https://arxiv.org/abs/2404.01155
• [13] Y. Yang, D. Liang, F. Zhao, Y. Li, S. Yin, and M. Yang, “Reliability evaluation of wind-storage power generation system considering fatigue failure of wind turbine gearbox,” in 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), 2022, pp. 3184–3189.
• [14] S.-H. Park, C.-J. Kim, and Y. Kang, “Evaluation of the supporting mounts of a three-in-one electric drive unit using a hybrid simulation model,” Machines, vol. 11, no. 11, 2023. [Online]. Available: https://www.mdpi.com/2075-1702/11/11/1026
• [15] G. Kothapalli, “An analogue recurrent neural network for trajectory learning and other industrial applications,” in INDIN ’05. 2005 3rd IEEE International Conference on Industrial Informatics, 2005., 2005, pp. 462–467.
• [16] W. M. E. Ahmed M. Elmogy, Amany Sarhan, “An adaptive real-time third order sliding mode control for nonlinear systems,” Computers, Materials & Continua, vol. 72, no. 3, pp. 5629–5641, 2022. [Online]. Available: http://www.techscience.com/cmc/v72n3/47465
• [17] J. Inoue and K. Sato, “Wind speed measurement by an inexpensive and lightweight thermal anemometer on a small uav,” Drones, vol. 6, no. 10, 2022. [Online]. Available: https://www.mdpi.com/2504-446X/6/10/289
• [18] Innovative Ram Air Turbine for Airborne Power Generation, ser. Turbo Expo: Power for Land, Sea, and Air, vol. Volume 8: Microturbines, Turbochargers and Small Turbomachines; Steam Turbines, 06 2015. [Online]. Available: https://doi.org/10.1115/GT2015-4343