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Role of Artificial Intelligence in Large Wind Turbine Designs

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  • Galih Bangga

    (DNV, Rivergate 3, Temple Quay, Bristol BS1 6EW, UK)

Abstract

The utilization of wind energy across various sectors has increased significantly in recent decades [...]

Suggested Citation

  • Galih Bangga, 2025. "Role of Artificial Intelligence in Large Wind Turbine Designs," Energies, MDPI, vol. 18(19), pages 1-4, October.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:19:p:5292-:d:1765871
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    References listed on IDEAS

    as
    1. Martin Geibel & Galih Bangga, 2022. "Data Reduction and Reconstruction of Wind Turbine Wake Employing Data Driven Approaches," Energies, MDPI, vol. 15(10), pages 1-40, May.
    2. Galih Bangga, 2022. "Progress and Outlook in Wind Energy Research," Energies, MDPI, vol. 15(18), pages 1-5, September.
    3. Jinane Radi & Jesús Enrique Sierra-García & Matilde Santos & Carlos Armenta-Déu & Abdelouahed Djebli, 2024. "Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack Constraints," Energies, MDPI, vol. 17(24), pages 1-21, December.
    4. Yang, Kun & Deng, Xiaowei & Ti, Zilong & Yang, Shanghui & Huang, Senbin & Wang, Yuhang, 2023. "A data-driven layout optimization framework of large-scale wind farms based on machine learning," Renewable Energy, Elsevier, vol. 218(C).
    5. Chen, Chuan & Zhou, Jing-wei & Li, Fengming & Zhai, Endi, 2022. "Stall-induced vibrations analysis and mitigation of a wind turbine rotor at idling state: Theory and experiment," Renewable Energy, Elsevier, vol. 187(C), pages 710-727.
    6. Luo, Zhaohui & Wang, Longyan & Xu, Jian & Wang, Zilu & Yuan, Jianping & Tan, Andy C.C., 2024. "A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements," Energy, Elsevier, vol. 294(C).
    7. Zhang, Zihang & Li, Jiayi & Lei, Zhenyu & Zhu, Qianyu & Cheng, Jiujun & Gao, Shangce, 2024. "Reinforcement learning-based particle swarm optimization for wind farm layout problems," Energy, Elsevier, vol. 313(C).
    8. Galih Bangga, 2025. "Sensitivity of Dynamic Stall Models to Dynamic Excitation on Large Flexible Wind Turbine Blades in Edgewise Vibrations," Energies, MDPI, vol. 18(3), pages 1-27, January.
    9. Lee, Namkyoung & Woo, Joohyun & Kim, Sungryul, 2025. "A deep reinforcement learning ensemble for maintenance scheduling in offshore wind farms," Applied Energy, Elsevier, vol. 377(PA).
    10. Wang, Zheng & Zeng, Tiansheng & Chu, Xuening & Xue, Deyi, 2023. "Multi-objective deep reinforcement learning for optimal design of wind turbine blade," Renewable Energy, Elsevier, vol. 203(C), pages 854-869.
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