DOI QR코드

DOI QR Code

Temperature Control of Ultrasupercritical Once-through Boiler-turbine System Using Multi-input Multi-output Dynamic Matrix Control

  • Moon, Un-Chul (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Kim, Woo-Hun (School of Electrical and Electronics Engineering, Chung-Ang University)
  • Received : 2010.09.27
  • Accepted : 2011.01.18
  • Published : 2011.05.02

Abstract

Multi-input multi-output (MIMO) dynamic matrix control (DMC) technique is applied to control steam temperatures in a large-scale ultrasupercritical once-through boiler-turbine system. Specifically, four output variables (i.e., outlet temperatures of platen superheater, finish superheater, primary reheater, and finish reheater) are controlled using four input variables (i.e., two spray valves, bypass valve, and damper). The step-response matrix for the MIMO DMC is constructed using the four input and the four output variables. Online optimization is performed for the MIMO DMC using the model predictive control technique. The MIMO DMC controller is implemented in a full-scope power plant simulator with satisfactory performance.

References

  1. J. Adams, D. R. Clark, J. R. Louis and J. P. Spanbauer, “Mathematical Modeling of Once-Through Boiler Dynamics,” IEEE Transaction on Power Apparatus and Systems, Vol. 84, pp. 145-156, Feb. 1965.
  2. J. Franke and R. Kral, “Supercritical boiler technology for future market condition,” Parsons Conference, 2003
  3. S. J. Goidich, S. Wu, Z. Fan and A. C. Bose, “Design Aspects of the Ultra-Supercritical CFB Boiler,” International Pittsburgh Coal Conference, Pittsburgh, Sep. 12-15, 2005
  4. J. A. Rovnak and R. Corlis, "Dynamic Matrix based Control of Fossile Power Plant," IEEE Transactions on Energy Conversion, Vol. 6, No. 2, pp. 320-326, June 1991. https://doi.org/10.1109/60.79639
  5. K. Y. Lee, J. Heo, J. A. Hoffman, S. Kim and W. Jung, “Modified predictive optimal control using neural network-based combined model for large-scale power plants,” IEEE Power Engineering Society General Meeting, pp. 1-8, 2007.
  6. Z. Hua, H. Hua, J. Lu and T. Zhang, “Research and application of a new predictive control based on state feedback theory in power plant control system,” IEEE Congress on Evolutionary Computation, pp.4378-4385, September 25-28, 2007.
  7. A. Chaibakhsh, A. Ghaffari and A. Rezaeifar, “A New Approach for Temperature Control in Steam Power Plant,” 16th Mediterranean Conference on Control and Automation, Congress Centre, Ajaccio, France, June 25-27, 2008.
  8. P. J. Gawthrop and P. E. Nomikos, “Automatic tuning of commercial PID controllers for single loop and multiloop applications,” IEEE Control Systems Magazine, Vol. 10, No. 1, pp. 34-42, 1990.
  9. D. E. Seborg, “A perspective on advanced strategies for process control,” Modeling Identification and Control, Vol. 15, No. 3, pp.179-189, 1994. https://doi.org/10.4173/mic.1994.3.8
  10. J. Richalet, A. Rault, J. L. Testud and J. Papon, “Model Predictive Heuristic Control: Application to Industrial Processes,” Automatica, Vol. 14, No. 5, pp. 413-428, 1978. https://doi.org/10.1016/0005-1098(78)90001-8
  11. C. R. Culter and B. L. Ramaker, "Dynamic Matrix Control - A Computer Control Algorithm," Proc. of Joint Automatic Control Conference, paper wp5-b, San Francisco, CA, 1980.
  12. C. E. Garcia and A.M. Morshedi, "Quadratic Programming Solution of Dynamic Matrix Control (QDMC)," Chem. Eng. Commun., Vol. 46, pp. 73-87, 1986. https://doi.org/10.1080/00986448608911397
  13. D. Dougherty and D. Cooper, “A Practical Multiple Model Adaptive Strategy for Multivariable Model Predictive Control,” Control Engineering Practice, Vol. 11, pp. 649-664, 2003. https://doi.org/10.1016/S0967-0661(02)00170-3
  14. D. Dougherty and D. J. Cooper, “Tuning Guidelines of a Dynamic Matrix Controller for Integrating (Non-Self-Regulating) Processes,” Industrial & Engineering Chemistry Research, Vol. 42, pp. 1739-1752, 2003. https://doi.org/10.1021/ie020546p
  15. L. A. Sanchez, F. G. Arroyo and R. A. Villavicencio, “Dynamic Matrix Control of Steam Temperature in Fossil Power Plant,” IFAC Control of Power Plants and Power Systems, pp. 275-280, Cancun, Mexico, Dec. 1995.
  16. U.-C. Moon, S.-C. Lee and K. Y. Lee, "An Adaptive Dynamic Matrix Control of a Boiler-Turbine System Using Fuzzy Inference,” Proc. of International Conference on Intelligent Systems Application to Power Systems, Kaohsing, Taiwan, November 5-8, 2007, pp. 566-571.
  17. U.-C. Moon, J.-D. Lee, S.-C. Lee and K. Y. Lee, "An Adaptive Dynamic Matrix Control of a Boiler-Turbine System,” Proc. of the 17th IFAC World Congress, July 6-11, 2008, Seoul, Korea.
  18. U. C. Moon and K. Y. Lee, "Step-Response Model Development for Dynamic Matrix Control of a Drum-Type Boiler–Turbine System," IEEE Transactions on Energy Conversion, Vol. 24, No. 2, pp. 423-430, June 2009. https://doi.org/10.1109/TEC.2009.2015986
  19. Liangyu Ma, Yongjun Lin, Lee, K.Y., “Superheater steam temperature control for a 300MW boiler unit with Inverse Dynamic Process Models”, Power and Energy Society General Meeting, 2010 IEEE, Minneapolis, Jul 25-Jul 29, 2010
  20. Kwang Y. Lee, Joel H. Van Sickel, Jason A. Hoffman, Won-Hee Jung and Sung-Ho Kim, “Controller Design for a Large-Scale Ultrasupercritical Once-Through Boiler Power Plant”, IEEE Transactions on Energy Conversion, Vol. 25, No. 4, pp. 1063-1070, Dec. 2010. https://doi.org/10.1109/TEC.2010.2060488
  21. J. H. Lee, “Model Predictive Control in the Process Industries: Review, Current Status and Future Outlook," Proc. of the 2nd Asian Control Conference, Vol II, pp. 435-438, Seoul. July 22-25, 1997.
  22. D. E. Seborg, T. E. Edgar and D. A. Mellichamp, Process Dynamics and Control, John Willy & Suns, 1989.
  23. G. F. Franklin, J. D. Powell and A. Emami-Naeini, Feedback Control of Dynamic System, Prentice-Hall, 2002.

Cited by

  1. Multivariable constrained predictive control of main steam temperature in ultra-supercritical coal-fired power unit vol.88, pp.2, 2015, https://doi.org/10.1016/j.joei.2014.06.003
  2. Multi-model Predictive Control of Ultra-supercritical Coal-fired Power Unit vol.22, pp.7, 2014, https://doi.org/10.1016/j.cjche.2014.05.005
  3. Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules vol.16, pp.12, 2016, https://doi.org/10.3390/s16101709
  4. Dynamic modeling and solution algorithm of the evaporation system for the ultra-supercritical power plant vol.105, 2017, https://doi.org/10.1016/j.ijheatmasstransfer.2016.09.084
  5. Ultra-Supercritical Power Unit Steam Temperature Control Based on Model Predictive Control vol.291-294, pp.1662-7482, 2013, https://doi.org/10.4028/www.scientific.net/AMM.291-294.2240
  6. Steam Pressure Control of 1 000MW Ultra-Supercritical Coal-Fired Power Unit Based on Multi-Model Predictive Control pp.1995-8188, 2018, https://doi.org/10.1007/s12204-018-2015-9