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Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations

Citations

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Cited by:

  1. Zhiwen Deng & Chang Xu & Zhihong Huo & Xingxing Han & Feifei Xue, 2023. "Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model," Energies, MDPI, vol. 16(9), pages 1-20, May.
  2. Wang, Yu & Wei, Shanbi & Yang, Wei & Chai, Yi, 2023. "Adaptive economic predictive control for offshore wind farm active yaw considering generation uncertainty," Applied Energy, Elsevier, vol. 351(C).
  3. Göçmen, Tuhfe & Liew, Jaime & Kadoche, Elie & Dimitrov, Nikolay & Riva, Riccardo & Andersen, Søren Juhl & Lio, Alan W.H. & Quick, Julian & Réthoré, Pierre-Elouan & Dykes, Katherine, 2025. "Data-driven wind farm flow control and challenges towards field implementation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 216(C).
  4. Kenny-Jesús Flores-Huamán & Alejandro Escudero-Santana & María-Luisa Muñoz-Díaz & Pablo Cortés, 2024. "Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach," Mathematics, MDPI, vol. 12(15), pages 1-34, July.
  5. Wang, Yize & Liu, Zhenqing & Hu, Yilu & Bai, Guangpu, 2026. "A coherent power-load optimization algorithm for wind farm-level yaw control considering wake effects via deep neural network," Renewable Energy, Elsevier, vol. 257(C).
  6. Wang, Yize & Liu, Zhenqing & Yu, Zhongze, 2025. "An efficient optimization algorithm for active yaw control to increase wind farm power while considering load reduction," Energy, Elsevier, vol. 340(C).
  7. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
  8. Zhang, Jincheng & Zhao, Xiaowei, 2021. "Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning," Applied Energy, Elsevier, vol. 300(C).
  9. Shao, Yi-Xiao & Wang, Zhen-Fan & Naung, Shine Win & Zhang, Kai & Yao, Yufeng & Zhou, Dai, 2025. "Towards high-fidelity wind farm layout optimization using polynomial chaos expansion and Kriging model," Energy, Elsevier, vol. 338(C).
  10. Kim, Taewan & Kim, Changwook & Song, Jeonghwan & You, Donghyun, 2024. "Optimal control of a wind farm in time-varying wind using deep reinforcement learning," Energy, Elsevier, vol. 303(C).
  11. Asadzadeh, Seyed Mohammad & Andersen, Nils Axel, 2024. "Optimal operational planning of a bio-fuelled cogeneration plant: Integration of sparse nonlinear dynamics identification and deep reinforcement learning," Applied Energy, Elsevier, vol. 376(PA).
  12. Yu, Xiaobing & Lu, Yangchen, 2023. "Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization," Energy, Elsevier, vol. 284(C).
  13. 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).
  14. Mahmud, Sakib & Sayed, Aya Nabil & Himeur, Yassine & Nhlabatsi, Armstrong & Bensaali, Faycal, 2026. "A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PE).
  15. Chen, Yuanqing & Wang, Ding & Feng, Dachuan & Tian, Geng & Gupta, Vikrant & Cao, Renjing & Wan, Minping & Chen, Shiyi, 2025. "Three-dimensional spatiotemporal wind field reconstruction based on LiDAR and multi-scale PINN," Applied Energy, Elsevier, vol. 377(PC).
  16. Dong, Zhen & Li, Zhongguo & Liang, Zhongchao & Xu, Yiqiao & Ding, Zhengtao, 2021. "Distributed neural network enhanced power generation strategy of large-scale wind power plant for power expansion," Applied Energy, Elsevier, vol. 303(C).
  17. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
  18. Pawar, Suraj & Sharma, Ashesh & Vijayakumar, Ganesh & Bay, Chrstopher J. & Yellapantula, Shashank & San, Omer, 2022. "Towards multi-fidelity deep learning of wind turbine wakes," Renewable Energy, Elsevier, vol. 200(C), pages 867-879.
  19. Huang, Zishuo & Wu, Wenchuan, 2024. "An efficient solution for large offshore wind farm power optimization with the Porté-Agel wake model: Optimality and efficiency," Energy, Elsevier, vol. 306(C).
  20. Kadoche, Elie & Gourvénec, Sébastien & Pallud, Maxime & Levent, Tanguy, 2023. "MARLYC: Multi-Agent Reinforcement Learning Yaw Control," Renewable Energy, Elsevier, vol. 217(C).
  21. Dong, Guodan & Qin, Jianhua & Wu, Chutian & Xu, Chang & Yang, Xiaolei, 2026. "Reinforcement learning-enhanced genetic algorithm for wind farm layout optimization," Renewable Energy, Elsevier, vol. 259(C).
  22. Li, Rui & Zhang, Jincheng & Huang, Yubo & Zhao, Xiaowei, 2026. "FlowFormer: Toward a foundation model for full-flow-field wind farm wake modeling," Renewable Energy, Elsevier, vol. 256(PI).
  23. Zhang, Yubao & Chen, Xin & Gong, Sumei & Chen, Jiehao, 2023. "Collective large-scale wind farm multivariate power output control based on hierarchical communication multi-agent proximal policy optimization," Renewable Energy, Elsevier, vol. 219(P2).
  24. Han, Ji & Zhang, Di & Jia, Bohui & Xie, Longjie & Wan, Weijia & Tan, Junyang & Chen, Zhe, 2025. "Power loss minimization-oriented reactive power control for wind farm equipped with distributed energy storages using clustering-based data-driven method," Energy, Elsevier, vol. 328(C).
  25. Hou, Guolian & Zhang, Fan & Huang, Congzhi & Huang, Ting, 2026. "Multivariate modeling on wake-affected wind farms by two-stage hybrid graph neural network," Applied Energy, Elsevier, vol. 402(PB).
  26. Zhang, Teng & Xu, Xiaosen & Wang, Shuaishuai & Xing, Yihan & Dou, Peng & Ji, Renwei & Yang, Puyi, 2025. "Investigation of wake steering control effects on the dynamic responses of 15 MW semi-submersible floating wind farms," Renewable Energy, Elsevier, vol. 254(C).
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