IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i8p3334-d1630848.html

Artificial Neural Network-Based Feedforward-Feedback Control for Parabolic Trough Concentrated Solar Field

Author

Listed:
  • Bo An

    (School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450002, China
    Key Laboratory of Process Heat Transfer and Energy Saving of Henan Province, Zhengzhou 450002, China)

  • Qin Zhang

    (School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450002, China
    Key Laboratory of Process Heat Transfer and Energy Saving of Henan Province, Zhengzhou 450002, China)

  • Lu Li

    (School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450002, China
    Key Laboratory of Process Heat Transfer and Energy Saving of Henan Province, Zhengzhou 450002, China)

  • Fan Gao

    (Key Laboratory of Process Heat Transfer and Energy Saving of Henan Province, Zhengzhou 450002, China
    School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450002, China)

  • Ke Wang

    (School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450002, China
    Key Laboratory of Process Heat Transfer and Energy Saving of Henan Province, Zhengzhou 450002, China)

  • Jiaqi Yang

    (School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450002, China
    Key Laboratory of Process Heat Transfer and Energy Saving of Henan Province, Zhengzhou 450002, China)

Abstract

The intermittency and fluctuation of solar irradiation pose challenges to the stable control of PTC collector loops. Therefore, this study proposes an Artificial Neural Network-based Feedforward-Feedback (ANN-FF-FB) model, which integrates irradiation prediction, feedforward, and feedback regulation to form a composite control strategy for the solar collecting system. During step changes in solar irradiation intensity, this model can quickly and stably adjust the outlet temperature, with a response time one-quarter that of a conventional PID model, a maximum overshoot of only 0.5 °C, a steady-state error of 0.02 °C, and it effectively reduces the entropy production in the transient process, improving the thermodynamic performance. Additionally, the ANN-FF-FB model’s response time during setpoint temperature adjustment is one-third that of the PID model, with a steady-state error of 0.03 °C. Ultimately, the system temperature stabilizes at 393 °C, with efficiency increasing to 0.212, and the overshoot being less than 1 °C.

Suggested Citation

  • Bo An & Qin Zhang & Lu Li & Fan Gao & Ke Wang & Jiaqi Yang, 2025. "Artificial Neural Network-Based Feedforward-Feedback Control for Parabolic Trough Concentrated Solar Field," Sustainability, MDPI, vol. 17(8), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3334-:d:1630848
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/8/3334/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/8/3334/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kalogirou, Soteris A., 2012. "A detailed thermal model of a parabolic trough collector receiver," Energy, Elsevier, vol. 48(1), pages 298-306.
    2. Zhang, Shunqi & Liu, Ming & Zhao, Yongliang & Liu, Jiping & Yan, Junjie, 2021. "Dynamic simulation and performance analysis of a parabolic trough concentrated solar power plant using molten salt during the start-up process," Renewable Energy, Elsevier, vol. 179(C), pages 1458-1471.
    3. He, Ya-Ling & Qiu, Yu & Wang, Kun & Yuan, Fan & Wang, Wen-Qi & Li, Ming-Jia & Guo, Jia-Qi, 2020. "Perspective of concentrating solar power," Energy, Elsevier, vol. 198(C).
    4. Li, Lu & Li, Yinshi & Yu, Huajie & He, Ya-Ling, 2020. "A feedforward-feedback hybrid control strategy towards ordered utilization of concentrating solar energy," Renewable Energy, Elsevier, vol. 154(C), pages 305-315.
    5. Ebrahimi-Moghadam, Amir & Mohseni-Gharyehsafa, Behnam & Farzaneh-Gord, Mahmood, 2018. "Using artificial neural network and quadratic algorithm for minimizing entropy generation of Al2O3-EG/W nanofluid flow inside parabolic trough solar collector," Renewable Energy, Elsevier, vol. 129(PA), pages 473-485.
    6. Li, Lu & Sun, Jie & Li, Yinshi & He, Ya-Ling & Xu, Haojie, 2019. "Transient characteristics of a parabolic trough direct-steam-generation process," Renewable Energy, Elsevier, vol. 135(C), pages 800-810.
    7. Qiu, Yu & Xu, Yucong & Li, Qing & Wang, Jikang & Wang, Qiliang & Liu, Bin, 2021. "Efficiency enhancement of a solar trough collector by combining solar and hot mirrors," Applied Energy, Elsevier, vol. 299(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liang, Huaxu & Wang, Fuqiang & Yang, Luwei & Cheng, Ziming & Shuai, Yong & Tan, Heping, 2021. "Progress in full spectrum solar energy utilization by spectral beam splitting hybrid PV/T system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    2. Wang, Qiliang & Yao, Yao & Shen, Zhicheng & Yang, Hongxing, 2023. "A hybrid parabolic trough solar collector system integrated with photovoltaics," Applied Energy, Elsevier, vol. 329(C).
    3. Qiu, Yu & Xu, Yucong & Li, Qing & Wang, Jikang & Wang, Qiliang & Liu, Bin, 2021. "Efficiency enhancement of a solar trough collector by combining solar and hot mirrors," Applied Energy, Elsevier, vol. 299(C).
    4. Wang, Jikang & Zhang, Yuanting & Zhang, Weichen & Qiu, Yu & Li, Qing, 2022. "Design and evaluation of a lab-scale tungsten receiver for ultra-high-temperature solar energy harvesting," Applied Energy, Elsevier, vol. 327(C).
    5. Gharat, Punit V. & Bhalekar, Snehal S. & Dalvi, Vishwanath H. & Panse, Sudhir V. & Deshmukh, Suresh P. & Joshi, Jyeshtharaj B., 2021. "Chronological development of innovations in reflector systems of parabolic trough solar collector (PTC) - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    6. Ma, Haiwen & Liu, Peng & Huang, Lu & Ren, Tingting & Ge, Yanlin & Chen, Lingen, 2024. "Heat transfer enhancement in parabolic trough receiver based on exergy destruction minimization," Energy, Elsevier, vol. 313(C).
    7. Zhang, Qiang & Jiang, Kaijun, 2024. "Heat transport and load response characteristics of a molten salt solar tower power station engaged in peak regulation," Applied Energy, Elsevier, vol. 371(C).
    8. Camelia Stanciu & Dorin Stanciu & Adina-Teodora Gheorghian, 2017. "Thermal Analysis of a Solar Powered Absorption Cooling System with Fully Mixed Thermal Storage at Startup," Energies, MDPI, vol. 10(1), pages 1-19, January.
    9. Adis Puška & Miroslav Nedeljković & Branislav Dudić & Anđelka Štilić & Alexandra Mittelman, 2024. "Improving Agricultural Sustainability in Bosnia and Herzegovina through Renewable Energy Integration," Economies, MDPI, vol. 12(8), pages 1-15, July.
    10. Bai, Wengang & Li, Hongzhi & Zhang, Xuwei & Qiao, Yongqiang & Zhang, Chun & Gao, Wei & Yao, Mingyu, 2022. "Thermodynamic analysis of CO2–SF6 mixture working fluid supercritical Brayton cycle used for solar power plants," Energy, Elsevier, vol. 261(PB).
    11. Avila-Marin, Antonio L. & Fernandez-Reche, Jesus & Carballo, Jose Antonio & Carra, Maria Elena & Gianella, Sandro & Ferrari, Luca & Sanchez-Señoran, Daniel, 2022. "CFD analysis of the performance impact of geometrical shape on volumetric absorbers in a standard cup," Renewable Energy, Elsevier, vol. 201(P1), pages 256-272.
    12. El Kouche, Amal & Ortegón Gallego, Francisco, 2022. "Modeling and numerical simulation of a parabolic trough collector using an HTF with temperature dependent physical properties," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 430-451.
    13. Ma, Ruihua & Ma, Dongyan & Ma, Ruijiang & Long, Enshen, 2022. "Theoretical and experimental analysis of temperature variation of V–Ti black ceramic solar collector," Renewable Energy, Elsevier, vol. 194(C), pages 1153-1162.
    14. Kumaresan, G. & Sudhakar, P. & Santosh, R. & Velraj, R., 2017. "Experimental and numerical studies of thermal performance enhancement in the receiver part of solar parabolic trough collectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1363-1374.
    15. Li, Xueling & Li, Renfu & Chang, Huawei & Zeng, Lijian & Xi, Zhaojun & Li, Yichao, 2022. "Numerical simulation of a cavity receiver enhanced with transparent aerogel for parabolic dish solar power generation," Energy, Elsevier, vol. 246(C).
    16. Yang, S. & Sensoy, T.S. & Ordonez, J.C., 2018. "Dynamic 3D volume element model of a parabolic trough solar collector for simulation and optimization," Applied Energy, Elsevier, vol. 217(C), pages 509-526.
    17. Ajbar, Wassila & Parrales, A. & Huicochea, A. & Hernández, J.A., 2022. "Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    18. Fan, Ruijin & Wan, Minghan & Zhou, Tian & Zheng, Nianben & Sun, Zhiqiang, 2024. "Graphene-enhanced phase change material systems: Minimizing optical and thermal losses for solar thermal applications," Energy, Elsevier, vol. 289(C).
    19. Jing-hu, Gong & Yong, Li & Jun, Wang & Lund, Peter, 2023. "Performance optimization of larger-aperture parabolic trough concentrator solar power station using multi-stage heating technology," Energy, Elsevier, vol. 268(C).
    20. Ma, Zhao & Li, Ming-Jia & Zhang, K. Max & Yuan, Fan, 2021. "Novel designs of hybrid thermal energy storage system and operation strategies for concentrated solar power plant," Energy, Elsevier, vol. 216(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3334-:d:1630848. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.