Application of artificial intelligence techniques in modeling and control of a nuclear power plant pressurizer system

Abstract In pressurized water reactor (PWR) nuclear power plants (NPPs) pressure control in the primary loops is fundamental for keeping the reactor in a safety condition and improve the generation process efficiency. The main component responsible for this task is the pressurizer. The pressurizer pressure control system (PPCS) utilizes heaters and spray valves to maintain the pressure within an operating band during steady state conditions, and limits the pressure changes during transient conditions. Relief and safety valves provide overpressure protection for the reactor coolant system (RCS) to ensure system integrity. Various protective reactor trips are generated if the system parameters exceed safe bounds. Historically, a proportional-integral-derivative (PID) controller is used in PWRs to keep the pressure in the set point, during those operation conditions. The purpose of this study is two-fold: first, to develop a pressurizer model based on artificial neural networks (ANNs); secondly, to develop fuzzy controllers for the PWR pressurizer modeled by the ANN and compare their performance with conventional ones. Data from a 2785 MWth Westinghouse 3-loop PWR simulator was used to test both the pressurizer ANN model and the fuzzy controllers. The simulation results show that the pressurizer ANN model responses agree reasonably well with those of the simulated power plant pressurizer, and that the fuzzy controllers have better performance compared with conventional ones.

[1]  Vineet Kumar,et al.  Real-Time Performance Evaluation of a Fuzzy PI + Fuzzy PD Controller for Liquid-Level Process , 2008 .

[2]  Jinfeng Peng,et al.  Design and optimization of fuzzy-PID controller for the nuclear reactor power control , 2009 .

[3]  Berndt Müller,et al.  Neural networks: an introduction , 1990 .

[4]  Enrico Zio,et al.  Dynamic simulation of a steam generator by neural networks , 1999 .

[5]  T. U. Bhatt,et al.  Application of fuzzy logic control system for regulation of differential pressure in Liquid Zone Control System , 2009 .

[6]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[7]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[8]  Kiam Heong Ang,et al.  PID control system analysis and design , 2006, IEEE Control Systems.

[9]  Han-Xiong Li,et al.  Conventional fuzzy control and its enhancement , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Hatice Akkurt,et al.  PWR system simulation and parameter estimation with neural networks , 2002 .

[11]  Belle R. Upadhyaya,et al.  Application of Neural Networks for Sensor Validation and Plant Monitoring , 1990 .

[12]  Martin T. Hagan,et al.  Neural network design , 1995 .

[13]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  P. Pivonka Comparative analysis of fuzzy PI/PD/PID controller based on classical PID controller approach , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).