A reinforcement learning based blade twist angle distribution searching method for optimizing wind turbine energy power
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DOI: 10.1016/j.energy.2020.119148
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Cited by:
- Kashif Sohail & Hooman Farzaneh, 2022. "Model for Optimal Power Coefficient Tracking and Loss Reduction of the Wind Turbine Systems," Energies, MDPI, vol. 15(11), pages 1-19, June.
- Shin, Heesoo & Rüttgers, Mario & Lee, Sangseung, 2023. "Effects of spatiotemporal correlations in wind data on neural network-based wind predictions," Energy, Elsevier, vol. 279(C).
- Zsolt Čonka & Ľubomír Beňa & Róbert Štefko & Marek Pavlík & Peter Holcsik & Judith Pálfi, 2022. "Wind Turbine Power Control According to EU Legislation," Energies, MDPI, vol. 15(22), pages 1-21, November.
- Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
- Fang, Jianhao & Hu, Weifei & Liu, Zhenyu & Chen, Weiyi & Tan, Jianrong & Jiang, Zhiyu & Verma, Amrit Shankar, 2022. "Wind turbine rotor speed design optimization considering rain erosion based on deep reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
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Keywords
Wind turbine blade; Blade design; Twist angle distribution; Optimization method; Reinforcement learning; Rapid optimization;All these keywords.
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