Offline Parameter Optimization of Steam Generator Liquid Level Control System Based on NSGA-II Algorithm

Sun Zhejun; Wei Xinyu; Zhang Nan; Li Mingqian; Zhang Ruiping; Wang Yulong; Sun Peiwei

doi:10.13832/j.jnpe.2024.090057

Article Contents

Article Navigation > Nuclear Power Engineering > 2024 > Accepted Manuscript

Sun Zhejun, Wei Xinyu, Zhang Nan, Li Mingqian, Zhang Ruiping, Wang Yulong, Sun Peiwei. Offline Parameter Optimization of Steam Generator Liquid Level Control System Based on NSGA-II Algorithm[J]. Nuclear Power Engineering. doi: 10.13832/j.jnpe.2024.090057

Citation:

Sun Zhejun, Wei Xinyu, Zhang Nan, Li Mingqian, Zhang Ruiping, Wang Yulong, Sun Peiwei. Offline Parameter Optimization of Steam Generator Liquid Level Control System Based on NSGA-II Algorithm[J]. Nuclear Power Engineering. doi: 10.13832/j.jnpe.2024.090057

Citation:

Sun Zhejun, Wei Xinyu, Zhang Nan, Li Mingqian, Zhang Ruiping, Wang Yulong, Sun Peiwei. Offline Parameter Optimization of Steam Generator Liquid Level Control System Based on NSGA-II Algorithm[J]. Nuclear Power Engineering. doi: 10.13832/j.jnpe.2024.090057

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Offline Parameter Optimization of Steam Generator Liquid Level Control System Based on NSGA-II Algorithm

doi: 10.13832/j.jnpe.2024.090057

1.
School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, 710049, China
2.
China Nuclear Power Engineering Co., Beijing, 100840, China

Received Date: 2024-09-15
Rev Recd Date: 2024-12-25

Available Online: 2025-01-14

Abstract

Abstract

The steam generator is an important equipment in nuclear power plants. Currently, the liquid level of the steam generator is mainly controlled by a fixed PID, so it is necessary to tune the PID parameters. Traditional parameter tuning methods require precise mathematical models, and when accurate model information cannot be obtained, the tuning effect is poor. Therefore, this article proposes a method for tuning the PID parameters of steam generator liquid, which extracts historical data for offline tuning. Firstly, the BP neural network is used to identify the model of the steam generator liquid level control system based on historical data. Then, the PID parameters are optimized offline on the established BP neural network model. The parameter tuning method adopts multi-objective genetic algorithm (NSGA-II), with the dynamic performance index of the control system as the objective function, adjusting the PID parameters to improve the control effect. The proposed algorithm was validated through simulation in Matlab/Simulink, and the results showed that the offline parameter optimized steam generator level control system had better overshoot and adjustment time than the original control system in different operating conditions, and had better control effects.
- Steam Generator,
- Identification,
- NSGA-II algorithm,
- PID parameter tuning

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References(12)

References

[1]	耿鹏程,史长青,孔祥松,等. 基于SPSA的蒸汽发生器液位MPC系统性能优化方法研究[J]. 核动力工程,2022, 43(5): 168-175, doi: 10.13832/j.jnpe.2022.05.0168.
[2]	苟晓龙. 基于NURES的蒸汽发生器液位控制优化与仿真研究[D]. 上海: 上海交通大学,2020,doi: 10.27307/d.cnki.gsjtu.2020.002636.
[3]	陈俊东. 基于改进遗传算法的四旋翼无人机PID控制参数整定[J]. 现代信息科技,2023, 7(11): 175-178, doi: 10.19850/j.cnki.2096-4706.2023.11.040.
[4]	金鑫,李东原,秦戈,等. 基于NSGA-Ⅱ算法的核反应堆功率LQG控制器设计[J]. 原子能科学技术,2022, 56(10): 2085-2095, doi: 10.7538/yzk.2021.youxian.0762.
[5]	涂环,陈辉. 基于多目标遗传算法的汽轮机转速PI控制器参数优化[J]. 武汉理工大学学报,2014, 36(2): 67-71. doi: 10.3963/j.issn.1671-4431.2014.02.013
[6]	HUI T Y, ZENG W J, YU T. Core power control of the ADS based on genetic algorithm tuning PID controller[J]. Nuclear Engineering and Design, 2020, 370: 110835. doi: 10.1016/j.nucengdes.2020.110835
[7]	SUN X Y, SONG F, YUAN J Q. Transient analysis and dynamic modeling of the steam generator water level for nuclear power plants[J]. Progress in Nuclear Energy, 2024, 170: 105103. doi: 10.1016/J.PNUCENE.2024.105103
[8]	周挺,程华,张今朝. 基于GA-BP神经网络的气压伺服系统辨识研究[J]. 机床与液压,2021, 49(6): 42-46, doi: 10.3969/j.issn.1001-3881.2021.06.009.
[9]	WU T, CAI S T, YAO Z H, et al. Design and optimization of the radial inflow turbogenerator for organic Rankine cycle system based on the Genetic Algorithm[J]. Applied Thermal Engineering, 2024, 253: 123749. doi: 10.1016/j.applthermaleng.2024.123749
[10]	KHAN A H, HOSSAIN S, HASAN M, et al. Development of an optimized thermodynamic model for VVER-1200 reactor-based nuclear power plants using genetic algorithm[J]. Alexandria Engineering Journal, 2022, 61(11): 9129-9148. doi: 10.1016/j.aej.2022.02.052
[11]	XU J S, TANG H, WANG X, et al. NSGA-II algorithm-based LQG controller design for nuclear reactor power control[J]. Annals of Nuclear Energy, 2022, 169: 108931. doi: 10.1016/j.anucene.2021.108931
[12]	SUMAN G K, GUERRERO J M, ROY O P. Stability of microgrid cluster with Diverse Energy Sources: a multi-objective solution using NSGA-Ⅱ based controller[J]. Sustainable Energy Technologies and Assessments, 2022, 50: 101834. doi: 10.1016/j.seta.2021.101834