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Thermal Science - Online First

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Simulation research on the grid connected generation system of solar thermal power generation

Objective: To improve the conversion efficiency of photovoltaic power generation system, improve the power quality, and promote the optimization and development of solar thermal power generation. Methods: In this study, firstly, voltage feedback, power feedback, disturbance observation and conductance increment are used to track the maximum power of photovoltaic cells. After that, the problems and shortcomings of incremental conductance method are further optimized and improved. Then, the control of maximum power point tracking (MPPT) is expanded. Finally, in the Matlab software, the simulation model of power generation system is built. Results: The synchronization time of voltage and current is less than 0.1s. The current voltage and grid current voltage have the same frequency and phase. In the case of reactive power, the power factor of the system is close to 1. There is a certain relationship between the wave form of the active power and the illumination, and the illumination change will be found. The active power will also change with it, and the changing waveform is very similar to the light waveform, or even close to it. Conclusion: The simulation experiment shows that the optimized power generation system has great advantages and effectiveness, its dynamic response ability is very strong, and the output waveform of the power generation system can change with the sun light, which can effectively prevent the interference from the environment.
PAPER REVISED: 2020-01-09
PAPER ACCEPTED: 2020-01-25
  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.