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

Bo An, Qin Zhang, Lu Li (), Fan Gao, Ke Wang and Jiaqi Yang
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Bo An: School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450002, China
Qin Zhang: School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450002, China
Lu Li: School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450002, China
Fan Gao: Key Laboratory of Process Heat Transfer and Energy Saving of Henan Province, Zhengzhou 450002, China
Ke Wang: School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450002, China
Jiaqi Yang: School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450002, China

Sustainability, 2025, vol. 17, issue 8, 1-17

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.

Keywords: concentrating solar energy; parabolic trough collector; composite control; artificial neural network (ANN) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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