Skip to main content

CFBB PID Controller Tuning with Probability based Binary Particle Swarm Optimization Algorithm

  • Conference paper
Life System Modeling and Intelligent Computing (ICSEE 2010, LSMS 2010)

Abstract

The high combustion efficiency, extensive fuel flexibility and environment friendly characteristics have made circulating fluidized bed boiler (CFBB) an alternate choice for coal fired thermal power plants for clean energy production. But CFBB is a highly nonlinear and complex combustion system because of coupling characteristics and time delays. PID controller tuning of such a complex system with traditional tuning methods cannot meet required control performance. In this paper, a new variant of binary particle swarm optimization algorithm (PSO), called probability based binary PSO is presented to tune the parameters of CFBB. The simulation results show that PBPSO can effectively optimize the controller parameters and achieve s a better control performance than those based on that of a standard discrete binary PSO and a modified binary PSO.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Lu, C.M.: Equipment and operation of circulating fluidized bed boiler, pp. 101–158. China Electric Power Press, Beijing (2003)

    Google Scholar 

  2. Ma, S.X., Yang, X.Y.: Study on dynamic characteristics of the combustion system of circu-lating fluidized bed boilers. Proceedings of the CSEE 26(9), 1–6 (2006)

    Google Scholar 

  3. Ma, S.X., Xue, Y.L.: Multi variable control of circulating fluidized bed boilers com busti on system. Journal of Power Engineering 27(4), 528–532 (2007)

    Google Scholar 

  4. Shinskey, F.G.: Process Control Systems: Application, Design, and Tuning. McGraw-Hill, New York (1988)

    Google Scholar 

  5. Yoon, M.H., Shin, C.H.: Design of online auto-tuning PID controller for power plant process control. System and communication research laboratory Korea electric power research institute 103-16 Munji-dong yusung-ku, Taejon, korea, pp. 305–380 (1997)

    Google Scholar 

  6. Ziegler, J.G., Icholos, N.B.: Optimum setting for automatic controllers. Trans. ASME 65, 433–444 (1943)

    Google Scholar 

  7. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. IEEE Int. Conf. Neural Networks, Perth, Australia, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  8. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108 (1997)

    Google Scholar 

  9. Shen, Q., Jiang, J.H.: Modified Particle Swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angoitensin II antagonists. European Journal of Pharmaceutical Sciences 22, 145–152 (2004)

    Google Scholar 

  10. Hu, Y.M., Zhang, G.Z., Chen, Y.F.: Neural network decoupling control strategy and its applications in combustion system of fluidized bed boiler. J. Electric Power Automation Equipment 23, 7–10 (2003)

    CAS  Google Scholar 

  11. Wang, L., Yu, J.S.: Fault feature selection based on modified binary PSO with mutation and its application in chemical process fault diagnosis. In: Wang, L., Chen, K., Ong, Y. S. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 832–840. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  12. Zhen, L.L., Wang, L., Huang, Z.Y.: Probability-based Binary Particle Swarm Optimization Algorithm and Its Application to WFGD Control. In: 2008 International Conference on Computer Science and Software Engineering (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Menhas, M.I., Wang, L., Pan, H., Fei, M. (2010). CFBB PID Controller Tuning with Probability based Binary Particle Swarm Optimization Algorithm. In: Li, K., Li, X., Ma, S., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Communications in Computer and Information Science, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15859-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15859-9_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15858-2

  • Online ISBN: 978-3-642-15859-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics