The pressurizer has an important role to ensure the protected operation of pressurized water reactor (PWR) by keeping the reactor coolant system pressure among allowed tolerances. In this paper, a non-equilibrium two-region nonlinear pressurizer (PZR) model was linearized using Taylor technique to introduce the linear models of pressurizer transients for the controller design purposes. Taylor series expansion of functions around an equilibrium point was calculated. The same assumptions of the reference model are considered in linearized model. The linearized model consists of five states; rate of changes for: mass of steam &water in the PZR, mass of water in the PZR, mass of water in the PC, steam temperature in PZR, and water temperature in PZR. The nonlinear and linear PZR models was implemented in MATLAB/Simulink. The linearized model of PZR is verified. The performance of linearized model of PZR and nonlinear model is consistent with small and acceptable errors. The closed-loop of the linearized model of PZR pressure and water level is implemented and investigated using standard Proportion-Integration-Differentiation (PID) controllers. Simulation results and the evaluation performance indicate that the proposed linear model of PZR is efficient, and it can be used for control purposes.

The pressure control in the pressurized water reactor (PWR) primary loop is key to secure the safe operation. The pressurizer (PZR) unit is responsible for attaining this task. Thus, the PZR unit's modeling is an important issue for the tracking control purpose and performance analysis concerning the turbine load. This paper develops a mathematical model to accurately predict the PZR unit pressure in the normal and load power changes. The model is a nonequilibrium three region model developed based on the thermodynamics of mass and energy for the water and steam in the PZR. The variations of the pressure and the temperature due to thermodynamic variables (enthalpy, density) and the mechanical work effect are considered in the developed model. The model also addresses other thermodynamic processes such as bulk flashing, spray condensation, interface condensation, wall condensation, and rainout flow. The inlet and outlet flow rates of the primary circuit (PC) and the average temperature and the hot leg temperature are included in the developed model. Based on data generated from the VVER-1200 simulator, parameter estimation, verification, and validation of the developed model through load power changes are achieved. Besides, a comparison with other models given in literature has been performed. This comparison indicated that the developed model is more sensitive against the minimum variation of PZR dynamics. Finally, a closed-loop PZR pressure control system, including a conventional Proportional Integral Derivative (PID) controller, was designed to test the control purpose's developed model. The simulation results over typical load change transients have been demonstrated the feasibility, effectiveness, and accuracy of the developed nonlinear model of PZR for dynamic modeling and control purposes.

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Fine control of output power for nuclear power plants is the essential goal for safe operation. In this work, a Fuzzy analytical proportional-integral-derivative (FPID) controller with different configurations is designed to adjust and control the pressure of the PZR system. The stability analysis of the FPID controller with variable gains is established, and conditions for bounded-input bounded-output stability conditions (BIBO) are derived using the small gain theory. Two scenarios are applied for evaluating the dynamic response of applied controllers. In addition, performance indices are compared between the PZR model and data measured from the PCtran VVER-1200 simulator. Finally, a simulation platform is developed for MATLAB / Simulink to implement the three-region nonlinear non-equilibrium PZR model and the designed pressure controllers. The analysis and evaluation results showed good durability of the designed controllers and satisfactory performance of the control. These results further show that the nonlinear PZR model is accurate, feasible, and valuable for dynamic simulation and control unit design.