Skip to main content
SpringerLink
Log in

An Improved Neuro-fuzzy Generalized Predictive Control of Ultra-supercritical Power Plant

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

A generalized predictive control method based on neuro-fuzzy network (NFN-GPC) is presented for a 1000-MW ultra-supercritical (USC) power plant to improve control performance. First, to decrease the nonlinearity, local linear models are elaborately constructed for approximating the studied system by virtue of neuro-fuzzy network (NFN). Second, a compensation mechanism is delicately developed to further increase the accuracy of local models. Through gauss function and B-spline function, the memberships of local models which represent the weights of local regions are determined. Finally, based on the obtained models, a multi-variable generalized predictive controller is designed to realize the optimal control over the whole operating region combined with the membership of the current neuro-fuzzy network. This scheme closely connects engineering with artificial intelligence; compared with traditional generalized predictive control, the merit of proposed NFN-GPC is that it can capture the details over the whole operating range which can get more accurate and faster control effects. The simulation results show that the proposed neuro-fuzzy generalized predictive control method can achieve the satisfactory performance even in the case of strong coupling and nonlinearity. In conclusion, the proposed method is an effective long-term approach to control the USC power plant.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Surywanshi GD, Pillai BBK, et al. 4-E analyses of chemical looping combustion based subcritical supercritical and ultra-supercritical coal-fired power plants. Energy Conv Manag. 2019;200:112050.

  2. Beér JM. High efficiency electric power generation: the environmental role. Prog Energy Combust Sci. 2007;33(2):107–34.

    Article  Google Scholar 

  3. Danilo HD, et al. Exergoenvironmental analysis of a ultra-supercritical coal-fired power plant. J Clean Prod. 2019;231(10):671–82.

    Google Scholar 

  4. Li Y, Ang KH, Chong GCY. Patents software and hardware for PID control: an overview and analysis of the current art. IEEE Control Syst Mag. 2006;26(1):42–54.

    Article  Google Scholar 

  5. Silva RN, Shirley PO, Lemos JM, Goncalves AC. Adaptive regulation of super-heated steam temperature: a case study in an industrial boiler. Contr Eng Pract. 2000;8(8):1405–15.

    Article  Google Scholar 

  6. Clarke DW, Mohtad C, Tuffs PS. Generalized predictive control-part I. Automatica. 1987;23(2):137–48.

    Article  Google Scholar 

  7. Clarke DW, Mohtad C, Tuffs PS. Generalized predictive control-part 2. Automatica. 1987;23(2):149–60.

    Article  Google Scholar 

  8. Hogg BW, El-Rabaie NM. Multivariable generalized predictive control of a boiler system. IEEE Trans Energy Convers. 1991;6(2):282–8.

    Article  Google Scholar 

  9. Zhang H, Liu Y, Dai J, Wang Y. Command filter based adaptive fuzzy finite-time control for a class of uncertain nonlinear systems with hysteresis. IEEE Trans Fuzzy Syst. 2020;29(9):2553–64. https://doi.org/10.1109/TFUZZ.2020.3003499.

    Article  Google Scholar 

  10. Peng H, Ozaki Y, Haggan-Ozaki V, Toyoda Y. A nonlinear exponential ARX model-based multivariable generalized predictive control strategy for thermal power plants. IEEE Trans Control Syst Technol. 2002;10(2):256–62.

    Article  Google Scholar 

  11. Rossiter JA, Ding Y. Interpolation methods in model predictive control: an overview. Int J Control. 2010;83(2):297–312.

    Article  MathSciNet  Google Scholar 

  12. Zandi Z, Mazinan AH. Maximum power point tracking of the solar power plants in shadow mode through artificial neural network. Complex Intell Syst. 2019;5(3):315–30.

    Article  Google Scholar 

  13. Zhang H, Liu Y, Wang Y. Observer-based finite-time adaptive fuzzy control for nontriangular nonlinear systems with full-state constraints. IEEE Trans Cybern. 2020;51(3):1110–20. https://doi.org/10.1109/TCYB.2020.2984791.

    Article  Google Scholar 

  14. Farah L, Hussain A, et al. A highly-efficient fuzzy-based controller with high reduction inputs and membership functions for a grid-connected photovoltaic system. IEEE Access. 2020;8:163225–37.

    Article  Google Scholar 

  15. Sumi AI, Zohora MF, et al. fASSERT: A fuzzy assistive system for children with autism using Internet of things. In: Informatics B, editor., et al., Cham. Switzerland: Springer; 2018. p. 403–12.

    Google Scholar 

  16. Kaiser MS, Chowdhury ZI, et al. A neuro-fuzzy control system based on feature extraction of surface electromyogram signal for solar-powered wheelchair. Cogn Comput. 2016;8:946–54.

    Article  Google Scholar 

  17. Liu XJ, Kong XB, et al. Modeling of a 1000 MW power plant ultra super-critical boiler system using fuzzy-neural network methods. Energy Conv Manag. 2013;65:518–27.

    Article  Google Scholar 

  18. Liu XJ, Guan P, Chan CW. Nonlinear multivariable power plant coordinate control by constrained predictive scheme. IEEE Trans Control Syst Technol. 2010;8(5):1116–25.

    Article  Google Scholar 

  19. Strusnik D, Avsec J. Artificial neural networking and fuzzy logic exergy controlling model of combined heat and power system in thermal power plant. Energy. 2015;80(1):318–30.

    Article  Google Scholar 

  20. Lin FJ, Tan KH, Luo WC, et al. Improved LVRT performance of pv power plant using recurrent wavelet fuzzy neural network control for weak grid conditions. IEEE Access. 2020;8:69346–58. https://doi.org/10.1109/ACCESS.2020.2984803.

    Article  Google Scholar 

  21. Kong XB, Liu XJ, Lee KY. An effective nonlinear multivariable HMPC for USC power plant incorporating NFN-based modeling. IEEE Trans Ind Inform. 2016;12(2):555–66.

    Article  Google Scholar 

  22. Kong XB, Liu XJ, Lee KY. Nonlinear multivariable hierarchical model predictive control for boiler-turbine system. Energy. 2015;93(1):309–22.

    Article  Google Scholar 

  23. Wang GL, et al. Multivariable constrained predictive control of main steam temperature in ultra-supercritical coal-fired power unit. J Energy Inst. 2015;88(2):181–7.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the reviewers for their valuable comments that considerably contributed to improving this paper.

Funding

This study was partially funded by National Natural Science Foundation of China (No. 61833011, 62173218), and International Corporation Project of Shanghai Science and Technology Commission under Grant 21190780300.

Author information

Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 201900, China

    Chuanliang Cheng, Chen Peng, Yusen Gang & Hanyu Mi

  2. School of Control and Computer Engineering, North China Electric Power University, Beijing, 102200, China

    Deliang Zeng

Authors
  1. Chuanliang Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Deliang Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yusen Gang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hanyu Mi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chen Peng.

Ethics declarations

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, C., Peng, C., Zeng, D. et al. An Improved Neuro-fuzzy Generalized Predictive Control of Ultra-supercritical Power Plant. Cogn Comput 13, 1556–1563 (2021). https://doi.org/10.1007/s12559-021-09949-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-021-09949-z

Keywords

Search

Navigation