Research of Intelligent Control Policy on Main Steam Temperature of Thermal Power Plant

Abstract:

In view of the nonlinear, time-variable, long delay, large inertia character of main steam temperature system, the difficult point of control is summarized. The status quo of application study on main steam temperature by fuzzy control, neural network and fuzzy neural network control, genetic algorithms is introduced. And taking "the 600 MW concurrent boiler load in 100%" as an example, carry on the main steam temperature control simulation by means of Matlab/Simulink software. In this simulation, four control strategies are taking for the simulation experiment, and comparing with the traditional PID control algorithm. The simulation results prove that fuzzy neural network control strategies have good robustness, fast response, short setting time, and great potential for the control of main steam temperature.

• Periodical: Applied Mechanics and Materials (Volumes 143-144)
• Pages: 307-311
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.143-144.307
• Online since: December 2011
• Authors: Yu Feng Luo, Lu Lu Liu
• Keywords: Fuzzy Control, Fuzzy Neural Network (FNN), Genetic Algorithm (GA), Main Steam Temperature Control, Neuron Network

References

[1] Kuipers B j. Qualitative Reasoning, Modeling and simulation with incomplete knowledge. Cambrige , MA , MIT Press, (1994).

[2] Linkens D A. Learning systems in intelligent control. IEEE proc. 2Control Theory Appl , 1996 , 143 (4) : 367~386.

[3] CHEN Li-jun, ZHOU Zheng-xing, ZHAO Li-li, in: Application of Control Strategy in Thermal Control of Power Plant, edited by Journal of Northeast Dianli Uiniversity Publications , Ji lin (2009), in press.

[4] ZHANG Jie-qi, LIU Hong, in: The Application of Neural Network- PID Controller in the Temperature Control of Superheated Steam in Power Plant, edited by Computer Engineering and Applications , Bei jing(2006), in press.

[5] LI Jian-jun, ZHANG Dong-jiao, MA Zhi-Jie, in: The Application of Fuzzy PID Controlling toMain Steam Temperature Control System, edited by Northeast Electric Power Technology, Shen yang(2008), in press.

[6] RONG Ya-jun, DOU Chun-xia, YUAN Shi-wen, in: A Design of Fuzzy Neural Network Forecast Controller on Superheat Temperature, edited by Proceedings of the Chinese Society for Electrical Engineering, Bei jing(2003), in press.

[7] Yubazaki N , Ashida T , Hirota K. Dynamic fuzzy control method and its application of inducation motor. IEEEInternational Conference on Fuzzy Systems , 1995 , 3 : 286~292.

DOI: 10.1109/fuzzy.1995.409820

[8] GU Jun, SHEN Jiong, CHEN Lai-jiu, in: Optimization of Fuzzy Logic Control Basedon Genetic Algorithm, edited by Journal of Southeast University(Natural Science Edition), Nan jing(1998), in press.

[9] WU Lv-bin, LUO Zi-xue, ZHOU Huai-chun, ZHANG Yun-tao, in: Summary of steam Temperature ControI and Its New AppHcation, edited by Power System Engincering, Hei long jiang(2009), in press.