scholarly journals Chaos Cooperative Particle Swarm Optimization Based Water Level Control for Nuclear Steam Generator

2016 ◽  
Vol 55 ◽  
pp. 04004
Author(s):  
Guimin Sheng ◽  
Yu Mu
Keyword(s):  
Particle Swarm Optimization ◽  
Water Level ◽  
Steam Generator ◽  
Particle Swarm ◽  
Level Control ◽  
Swarm Optimization

Parameter Optimization for AP1000 Steam Generator Feedwater Control System Using Particle Swarm Optimization Algorithm

2016 ◽  
Author(s):  
Shifa Wu ◽  
Pengfei Wang ◽  
Jiashuang Wan ◽  
Xinyu Wei ◽  
Fuyu Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Water Level ◽  
Steam Generator ◽  
Parameter Optimization ◽  
Particle Swarm ◽  
Operating Conditions ◽  
Control Performance ◽  
Level Control ◽  
Swarm Optimization

The U-tube Steam Generator (UTSG) of AP1000 Nuclear Power Plant (NPP) is the crucial component transferring heat from the primary loop to the secondary loop to make steam. The UTSG of AP1000 NPP is a highly complex, nonlinear and time-varying system and its parameters vary with operating conditions. Therefore, it is difficult and challenging to well control the water level of AP1000 UTSG by tuning the PID controller parameter in a traditional way, especially when the system is undergoing a sharp transient. To achieve better control performance, the Particle Swarm Optimization (PSO) algorithm was applied for the parameter optimization of the AP1000 UTSG feedwater control system in this study. First, the mathematical model of AP1000 UTSG was established and the objective function was developed with the system constraints considered. Second, the simulation platform was built and then the simulation was conducted in MATLAB/Simulink environment. Finally, the optimized parameters were obtained and the feedwater control system with optimized parameters was simulated against that without optimized. The simulation results demonstrate that optimized parameters of AP1000 UTSG feedwater control system can significantly improve the water level control performance with smaller overshoot and faster response. Therefore, the PSO based optimization method can be applied to optimizing AP1000 UTSG feedwater control system parameters to provide much better control capabilities.

Parameter Optimization of Steam Generator Feedwater Controller Based on Particle Swarm Optimization

2013 ◽  
Vol 291-294 ◽  
pp. 2496-2499
Author(s):  
Rui Xiang ◽  
Rui Yu ◽  
Zhi Wu Ke ◽  
Ke Long Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Water Level ◽  
Steam Generator ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Step Response ◽  
Swarm Optimization ◽  
Response Characteristics ◽  
Water Lever

The U-tube steam generator(SG), as the joint of primary and secondary circuits, is one of the crucial components in a PWR plant. Because of the swell-and-shrink phenomena, it is very difficult to control the water level of the steam generator. Presently the three-element controller in cascade configuration with two PID controller is widely use. But the determination of the six optimal PID parameters of the cascade controller is a major problem. This paper studies the application of the particle swarm optimization(PSO) methods in determining the parameters of cascade controller of SG water lever. The SG is modeled in Simulink and the PSO algorithm is implemented in MATLAB. Comparing with conventional PID parameter, the proposed method is more efficient in improving the step response characteristics while controlling the water level of SG.

scholarly journals Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Yoyok Dwi Setyo Pambudi ◽  
Wahidin Wahab ◽  
Benyamin Kusumoputro
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Process ◽  
Power Level ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Process Time ◽  
Level Control ◽  
Swarm Optimization ◽  
Inverse Control ◽  
Dic Technique

A neural network-direct inverse control (NN-DIC) has been simulated to automatically control the power level of nuclear reactors. This method has been tested on an Indonesian pool type multipurpose reactor, namely, Reaktor Serba Guna-GA Siwabessy (RSG-GAS). The result confirmed that this method still cannot minimize errors and shorten the learning process time. A new method is therefore needed which will improve the performance of the DIC. The objective of this study is to develop a particle swarm optimization-based direct inverse control (PSO-DIC) to overcome the weaknesses of the NN-DIC. In the proposed PSO-DIC, the PSO algorithm is integrated into the DIC technique to train the weights of the DIC controller. This integration is able to accelerate the learning process. To improve the performance of the system identification, a backpropagation (BP) algorithm is introduced into the PSO algorithm. To show the feasibility and effectiveness of this proposed PSO-DIC technique, a case study on power level control of RSG-GAS is performed. The simulation results confirm that the PSO-DIC has better performance than NN-DIC. The new developed PSO-DIC has smaller steady-state error and less overshoot and oscillation.

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