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

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  • 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
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