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AUTOMATIC CONTROL SYSTEM OF BOILER THERMAL ENERGY IN THERMAL POWER PLANT BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGY

ABSTRACT
Thermal processes tend to have large inertia and hysteresis, non-linearity, and slow time-varying. Therefore, the fixed-parameter proportional integral derivative conventional regulation system cannot meet the higher and higher control requirements in production. Based on this research background, the paper proposes an automatic control method for thermal boiler steam based on artificial intelligence technology. Through the real-time monitoring of the boiler, the state monitoring method is used to estimate the influence factors of the boiler, and the estimated error output is artificially supplemented to realize the accurate control of the boiler. After being put on the market, it is found that the control method proposed in the article can overcome the randomness and inertia of the temperature and accurately realize the temperature control of the boiler. Moreover, compared with the traditional proportional integral derivative control, this method is more effective.
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PAPER SUBMITTED: 1970-01-01
PAPER REVISED: 2021-01-19
PAPER ACCEPTED: 2021-02-05
PUBLISHED ONLINE: 2021-07-31
DOI REFERENCE: https://doi.org/10.2298/TSCI2104141Z
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2021, VOLUME 25, ISSUE Issue 4, PAGES [3141 - 3148]
REFERENCES
  1. Han, J., Active Disturbance Rejection Controller and its Application (in Chinese), Control and Decision, 18 (1998), 1, pp. 18-23
  2. Chandrasekharan, S., et al., Operational Control of an Integrated Drum Boiler of a Coal Fired Thermal Power Plant, Energy, 159 (2018), 9, pp. 977-987
  3. Wu, S., Study and Evaluation of Clustering Algorithm for Solubility and Thermodynamic Data of Glycerol Derivatives, Thermal Science, 23 (2019), 5, pp. 2867-2875
  4. Madrigal-Espinosa, G., et al., Fault Detection and Isolation System for Boiler-Turbine Unit of a Thermal Power Plant, Electric Power Systems Research, 148 (2017), 7, pp. 237-244
  5. Cheng, Y., et al., Thermalnet: A Deep Reinforcement Learning-Based Combustion Optimization System for Coal-Fired Boiler, Engineering Applications of Artificial Intelligence, 74 (2018), 9, pp. 303-311
  6. Ismatkhodzhaev, S. K., Kuzishchin, V. F., Enhancement of the Efficiency of the Automatic Control System to Control the Thermal Load of Steam Boilers Fired with Fuels of Several Types, Thermal Engineering, 64 (2017), 5, pp. 387-398
  7. Wang, Y., et al., Flexible Operation of Retrofitted Coal-Fired Power Plants to Reduce Wind Curtailment Considering Thermal Energy Storage, IEEE Transactions on Power Systems, 35 (2020), 2, pp. 1178-1187
  8. Liu, B., et al., Economic Dispatch of Combined Heat and Power Energy Systems Using Electric Boiler to Accommodate Wind Power, IEEE Access, 8 (2020), 99, pp. 41288-41297
  9. Wu, S., Construction of Visual 3-D Fabric Reinforced Composite Thermal Performance Prediction System, Thermal Science, 23 (2019), 5, pp. 2857-2865
  10. Sabanin, V. R., et al., Study of Connected System of Automatic Control of Load and Operation Efficiency of a Steam Boiler with Extremal Controller on a Simulation Model, Thermal Engineering, 64 (2017), 2, pp. 151-160

© 2024 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