DOI: 10.57760/sciencedb.15448
title: Research on setpoint decision of PWR control system based on PSO algorithm
title: 基于粒子群算法的压水堆控制系统设定值决策研究

description: [Background]: Analog-based instrumentation and control systems in nuclear power plants (NPP) are being progressively supplanted by comprehensive digital technologies, enabling the deployment of sophisticated and efficient advanced control methodologies. Although there are studies on improving the control performance of pressurized water reactor (PWR) NPP control systems by advanced control algorithms, most of them only focus on the control system itself without considering the interconnection and coupling among multiple control systems. [Purpose]: This study aims to comprehensively consider the coupling effect among control systems, coordinate multiple control systems from the top level to optimize the overall control performances and achieve better task execution results, a setpoint decision optimization system is proposed. [Methods]: The intelligent decision system for PWR control system was optimized based on particle swarm optimization (PSO) method. The decision objective function and operation constraint conditions of the intelligent decision system were proposed. Considering the actual operation of PWR, the system optimized the setpoint offline and the intelligent decision operation was performed online according to the operation condition to provide the directions and amplitudes of the control targets for the underlying control systems. The typical operation process of the PWR NPP was taken as an example to carry out the simulation of the deigned intelligent decision-making system, and the simulation results were analyzed. [Results]: Compared with the control scheme using traditional setpoints, the ITSE (Integral of Time multiplied by the Square Error) value of average coolant temperature, pressurizer level, pressurizer pressure and steam generator level was decreased by 58.9%, 67.7%, 99.9% and 83.3%, respectively. The peak value was decreased by 62.4%, 3.0%, 100% and 66.3% respectively. [Conclusions]: The simulation results show that the system proposed in this paper can effectively reduce the ITSE and peak value of the system. The overall control performances and safety margin of the control systems of PWR NPP are improved. In practical engineering practice, it can be combined with digital twin technology to use the characteristics of the digital twin that can synchronously reflect the real state of the system for more accurate online setpoint optimization, so as to achieve better control performance. 
description: [Background]: Analog-based instrumentation and control systems in nuclear power plants (NPP) are being progressively supplanted by comprehensive digital technologies, enabling the deployment of sophisticated and efficient advanced control methodologies. Although there are studies on improving the control performance of pressurized water reactor (PWR) NPP control systems by advanced control algorithms, most of them only focus on the control system itself without considering the interconnection and coupling among multiple control systems. [Purpose]: This study aims to comprehensively consider the coupling effect among control systems, coordinate multiple control systems from the top level to optimize the overall control performances and achieve better task execution results, a setpoint decision optimization system is proposed. [Methods]: The intelligent decision system for PWR control system was optimized based on particle swarm optimization (PSO) method. The decision objective function and operation constraint conditions of the intelligent decision system were proposed. Considering the actual operation of PWR, the system optimized the setpoint offline and the intelligent decision operation was performed online according to the operation condition to provide the directions and amplitudes of the control targets for the underlying control systems. The typical operation process of the PWR NPP was taken as an example to carry out the simulation of the deigned intelligent decision-making system, and the simulation results were analyzed. [Results]: Compared with the control scheme using traditional setpoints, the ITSE (Integral of Time multiplied by the Square Error) value of average coolant temperature, pressurizer level, pressurizer pressure and steam generator level was decreased by 58.9%, 67.7%, 99.9% and 83.3%, respectively. The peak value was decreased by 62.4%, 3.0%, 100% and 66.3% respectively. [Conclusions]: The simulation results show that the system proposed in this paper can effectively reduce the ITSE and peak value of the system. The overall control performances and safety margin of the control systems of PWR NPP are improved. In practical engineering practice, it can be combined with digital twin technology to use the characteristics of the digital twin that can synchronously reflect the real state of the system for more accurate online setpoint optimization, so as to achieve better control performance.