Effect of short cloud shading on the performance of parabolic trough solar power plants: motorized vs manual valves
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DOI: 10.1016/j.renene.2019.04.094
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- García, Jesús M. & Padilla, Ricardo Vasquez & Sanjuan, Marco E., 2016. "A biomimetic approach for modeling cloud shading with dynamic behavior," Renewable Energy, Elsevier, vol. 96(PA), pages 157-166.
- Prasad, Abhnil A. & Taylor, Robert A. & Kay, Merlinde, 2015. "Assessment of direct normal irradiance and cloud connections using satellite data over Australia," Applied Energy, Elsevier, vol. 143(C), pages 301-311.
- Ashley, Thomas & Carrizosa, Emilio & Fernández-Cara, Enrique, 2017. "Optimisation of aiming strategies in Solar Power Tower plants," Energy, Elsevier, vol. 137(C), pages 285-291.
- Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
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- Song, Yuhui & Wang, Jiaxing & Zhang, Junli & Li, Yiguo, 2024. "Temperature homogenization control of parabolic trough solar collector field based on hydraulic calculation and extended Kalman filter," Renewable Energy, Elsevier, vol. 226(C).
- Chanfreut, Paula & Maestre, José M. & Gallego, Antonio J. & Annaswamy, Anuradha M. & Camacho, Eduardo F., 2023. "Clustering-based model predictive control of solar parabolic trough plants," Renewable Energy, Elsevier, vol. 216(C).
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