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In this study, the parameters of the feedwater control system (FWCS) of the OPR1000 type nuclear power plant (NPP) are optimized by response surface methodology (RSM) in order to acquire better level control performance from the FWCS. The objective of the optimization is to minimize the steam generator (SG) water level deviation from the reference...
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Context 1
... of '0' is given as the desirability function value for 50% deviation (the worst case) and the value of '1' is given for 0% deviation (the best case). For deviations between 0% and 50%, values between '0' and '1' are assigned. The desirability function has the typical form of [4]. Therefore, the desirability function for the index1 is given as Fig. 6. For index2, from the viewpoint of the level difference between the two SGs, the difference value may be -100% ~ +100%. A difference of 0% is the best case and a difference of 100% is the worst case. The desirability function for the index2 is given as Fig. 7 so that the degree of the SG level difference between the two SGs may be ...
Context 2
... following desirability functions are used: The first index is the degree of the SG level deviation from the setpoint and the second index is the degree of the SG level difference between the two SGs. For index1, from the viewpoint of the SG level deviation from the set point, the deviation value may be -44% ~ +56% and the maximum deviation value (absolute value) is chosen as 50%. The value of '0' is given as the desirability function value for 50% deviation (the worst case) and the value of '1' is given for 0% deviation (the best case). For deviations between 0% and 50%, values between '0' and '1' are assigned. The desirability function has the typical form of [4]. Therefore, the desirability function for the index1 is given as Fig. 6. For index2, from the viewpoint of the level difference between the two SGs, the difference value may be -100% ~ +100%. A difference of 0% is the best case and a difference of 100% is the worst case. The desirability function for the index2 is given as Fig. 7 so that the degree of the SG level difference between the two SGs may be forced to go to zero through the optimization. Then, a single composite response D is calculated according to Eq. ...
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Citations
Failure to control the steam generator (SG) water levels causes unexpected shutdowns in the Advanced Power Reactor (APR) 1400 nuclear power plants. This impacts maintenance, operational life, and safety critically. Therefore, it is vital to stabilize the SG water level at different power levels, particularly at low powers, by improving the performance of the proportional integral (PI) controller in the Feedwater Control System (FWCS). The PI controller designed using Ms-Constrained Integral Gain Optimization (MIGO) mitigated this problem significantly. In order to design this PI controller for the FWCS, the SG model was developed as a process model with a system identification algorithm in MATLAB. The algorithm processed data sets obtained from the thermal-hydraulic model by the Safety and Performance Analysis Code (SPACE). The MIGO and the Ziegler-Nichols methods were each used to produce a design for the PI controller, and the controllers were compared. Simulations were run in MATLAB for resulting designs. The PI controller designed by the MIGO is more stable than the controller using the Ziegler-Nichols approach. Finally, the PI MIGO controller was applied to an overall schematic block diagram of the FWCS. The simulations of the diagram in MATLAB Simulink demonstrate the PI MIGO controller offers superior control and enhanced robustness in the FWCS.
The aim of this work is to obtain the optimum controller parameters in advanced mechanical shim (MSHIM) control system of AP1000. The MSHIM control system is designed to allow the AP1000 nuclear steam supply system (NSSS) to perform power change operations automatically. For the MSHIM control system, the lead-time constant and lead/lag ratio in the average coolant temperature (Tavg) controller, and rate/lag constant and low gain of nonlinear gain unit in the power mismatch controller are considered decision variables through the sensitivity analysis when minimizing the overshoot in the nuclear power and maximum absolute deviation between average coolant temperature and its target value. The multi-objective problem with four decision variables is translated into two subproblems. The lead-time constant and lead/lag ratio in the Tavg controller are firstly optimized by non-dominated sorting genetic algorithm (NSGA-II) with the power mismatch controller defeated. Then rate/lag constant and low gain in the power mismatch controller is optimized by NSGA-II with the optimized parameters in the Tavg controller. Dynamic simulations are performed based on a fast-running NSSS Control & Analysis Platform (NCAP) in each iteration of the optimization process to calculate the objective functions. The 10 percent step load increase transient from 90% to 100% full power is selected as simulation case for the optimization. The calculation results demonstrate that much better reactor control capabilities can be provided by MSHIM control system when employing the optimized control parameters.
