Dissertation > Excellent graduate degree dissertation topics show

Sudy on Intelligent Control of Boiler-turbine Power System

Author: XiaoXueFei
Tutor: WuZhongQiang
School: Yanshan University
Course: Control Theory and Control Engineering
Keywords: Boiler-turbine power system RBF neural network Dynamic surface control Backstepping method H-infinity robust control Particle swarm algorithm Riccati algebra inequality
CLC: TM621
Type: Master's thesis
Year: 2013
Downloads: 0
Quote: 0
Read: Download Dissertation


Coal-fired power as the main force of power industry, its power generation efficiency is increasingly becoming the concerned focus by people. Power generation unit coordinated control directly affects the normal operation of the power plant. The practical and feasible control system is a powerful guarantee to improve the efficiency of power generation.Boiler-turbine power system is an important component of the thermal power units, the system has the characteristics of multiple input and output, nonlinear and strong coupling, etc. For boiler-turbine system, respectively, the terminal sliding mode controller and robust adaptive controller are designed. On the basis of this, research the higher order nonlinear complete mathematical model (including boiler, turbine and generator model) of power units, which has dynamic uncertainty then design the controller.First of all, the classic nonlinear inverse system method is proposed to realize the feedback linearization and decoupling of boiler-turbine power system. Considering the influence of modeling error and the parameters of the system can change in a wide range of operating condition, combined with the radial basis function (RBF) neural network method to identify the inverse system and reduce the modeling error through online learning. Then design the terminal sliding mode controller for the decoupling system in order to achieve the limited time convergence. Using lyapunov method to analytical the stability of the system, the simulation results show that the control system can work well in the wide range operating condition, which better than the classical inverse system control method.Secondly, put forward a kind of state feedback H-infinity robust control method according to the boiler-turbine power system which based on particle swarm algorithm. This paper come the H-infinity robust control design question down to solve the Riccati inequality algebra solution. Using the improved particle swarm algorithm combined with the linear matrix inequality (LMI) to solve the Riccati matrix algebra equation, get the state feedback controller which can guarantee the system is asymptotically stable. The simulation results show that the system has strong robustness for external disturbance, has good decoupling effect and tracking ability.Finally, the backstepping dynamic surface control method is used to design a robust adaptive controller for the boiler-turbine power generation unit. The system is decomposed into two smaller subsystems then the controller design was carried out on the two subsystems. In inverse design process to join the low-pass filter, which makes the design method simplified. Theoretical analysis and simulation results show that the controller can guarantee the closed-loop system global asymptotic stability, and has strong robustness for system parameters uncertainty, which can make the system stable with occurs breakdown.

Related Dissertations

  1. Study on Risk Identification and Evaluation of Manufacturing Green Products R & D,F205;F224
  2. Design of Positive Draw-back Motion of Wool Spinning Frame and Comparison of Prediction Models of Worsted Yarns Performances,TP183
  3. Based on sinter quality prediction systems and industrial application integration model,TF046.4
  4. Intelligent Decoupling and Control Research of Looper Height and Tension in Hot Continuous Rolling Mill,TP273
  5. Research of Cold-rolled Steel Sheet Surface Quality On-line Monitoring System,TG334.9
  6. The Research and Design of Medicine Decocting Machine System Based on Soft Sensor,TP273
  7. Research and Realization of Industry Injury Early Warning System Based on Web and Neural Networks,TP311.52
  8. AC Drive Sliding Control Algorithm,TP273
  9. Based on RBF artificial neural network in the application of PCB drilling process,TN405
  10. Networked Control System Fault Diagnosis and Fault Tolerant Control,TP273
  11. Linear guide system Elevator single electromagnetic levitation RBF neural network sliding mode control,TP273
  12. Optimization algorithm based on artificial intelligence Melt Index Prediction Modeling Optimization,TQ325.14
  13. Robust and adaptive unmanned helicopter Tolerant Control Technology,V249.1
  14. Robust research AQM network congestion control algorithm,TP393.06
  15. Research on Personal Credit Risk Evaluation System in Banking Based on Combined Forecast Model,F832.3
  16. RBF neural network based on Gaussian type function approximation and applications,O174.41
  17. Research on RBF Neural Networks Model and Learning Algorithm Based on Semi-Supervied and Multiple-Instance,O242.1
  18. The Research of Roasting Temperature Control System on Rotary Kiln for Iron Oxide Red,TQ622.15
  19. Study on Injection Molding Process Multiobjective Optimization and Quality Estimation Based on RBF Neural Network,TQ320.662
  20. The Research on Converter’s Control System of the Direct-Driven Wind Power Generation System,TM46
  21. Modeling on Fault Diagnosis of Generator Rotor Winding Inter-turn Short-circuit and Its Application Research,TM303.3

CLC: > Industrial Technology > Electrotechnical > Power generation, power plants > Power plant > Thermal power plants, thermal power stations
© 2012 www.DissertationTopic.Net  Mobile