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Objective: To improve the efficiency and stability of the solar thermal power generation system, and promote the optimization and development of solar thermal power generation grid connection. Methods: The working principle of the heat exchanger in the heat storage system is analyzed. Combined with the technological requirements of the system, the mathematical model of the heat exchanger is established by the mechanism modelling method. According to the inherent characteristics and control requirements of the heat storage system, the control schemes are proposed. The control strategies of different control algorithms, such as single-loop control, Smith predictive compensation control, cascade-Smith control, and feedforward-cascade-Smith control, are designed and adopted. The simulation model is established to obtain step response waveforms of different control systems. The advantages and disadvantages of different control strategies are comprehensively analyzed and compared. Results: After introducing the superheated steam mass-flow disturbance, the error of the single-loop control system increases. After adjusting the system to restore the oscillation state, the system error is high (10.24%). Smith predictive compensation control system fluctuates, with a peak time of 548 seconds
PAPER REVISED: 2020-01-09
PAPER ACCEPTED: 2020-01-25
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THERMAL SCIENCE YEAR 2020, VOLUME 24, ISSUE Issue 5, PAGES [3239 - 3248]
  1. Uniyal A., et al., Image processing and GIS techniques applied to high resolution satellite data for lineament mapping of thermal power plant site in Allahabad district, U.P. India. Geocarto International, 31 (2016), 9, pp. 956-965.
  2. M. Zhang, M., et al., Research on Compound Control Strategy of Wind/PV/Storage Hybrid Power Generation System. High Voltage Apparatus, 54 (2018), 1, pp. 64-72.
  3. Shaofei Wu. Construction of visual 3-d fabric reinforced composite thermal performance prediction system, Thermal Science, 23(2019), 5, pp.2857-2865.
  4. Surender S.R. Optimal power flow with renewable energy resources including storage. Electrical Engineering, 99 (2016), 2, pp. 1-11.
  5. Rumana R.A., et al., Simulating the thermal behavior in Lake Ontario using EFDC. Journal of Great Lakes Research, 42 (2016), 3, pp. 511-523.
  6. Lazića, V., et al., Selection and analysis of material for boiler pipes in a steam plant. Procedia Engineering, 149 (2016), pp. 216-223.
  7. Yanlei X., et al., A New Method of Computer Image Processing and Detection Based on AHP Analysis. Journal of Computational and Theoretical Nanoscience, 13 (2016), 7, pp. 4368-4372.
  8. Morteza D.J., et al., Effective Scheduling of Reconfigurable Microgrids With Dynamic Thermal Line Rating. IEEE Transactions on Industrial Electronics, 99 (2018), pp. 1-1.
  9. Soltani, A., et al., A new expert system based on fuzzy logic and image processing algorithms for early glaucoma diagnosis. Biomedical Signal Processing and Control, 40 (2018), pp. 366-377.
  10. Javed, A., et al., Smart Random Neural Network Controller for HVAC Using Cloud Computing Technology. IEEE Transactions on Industrial Informatics, 13 (2016), 1, pp. 1-1.
  11. Anna S. The Influence of Solar Power Plants on Microclimatic Conditions and the Biotic Community in Chilean Desert Environments. Environmental Management, 60 (2017), 2, pp. 1-13.
  12. Shaofei Wu, Mingqing Wang, Yuntao Zou. Bidirectional cognitive computing method supported by cloud technology, Cognitive Systems Research, 52(2018), pp. 615-621
  13. Chang, P., et al., A Deep Neural Network Based on ELM for Semi-supervised Learning of Image Classification. Neural Processing Letters, 48 (2017), 1, pp. 1-14.
  14. Steven Tay N.H., et al., Review on concentrating solar power plants and new developments in high temperature thermal energy storage technologies. Renewable & Sustainable Energy Reviews, 53 (2016), pp. 1411-1432.
  15. Huang, et al., Recognition of convolutional neural network based on CUDA Technology. Computer Science, 36 (2015), 15, pp. 179-181.

© 2023 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence