Offline Parameter Optimization of Steam Generator Liquid Level Control System Based on NSGA-II Algorithm
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摘要: 蒸汽发生器是核电厂的重要设备，目前蒸汽发生器液位主要采用固定比例-积分-微分（PID）的控制方式，因此需要对PID参数进行整定。传统的参数整定方法需要结合精确的数学模型，当无法获得准确模型信息时，整定效果较差。因此本文提出一种蒸汽发生器液PID参数整定方法，该方法通过提取历史数据进行离线整定。首先利用反向传播（BP）神经网络结合历史数据对蒸汽发生器液位控制系统进行模型辨识，之后在建立的BP神经网络模型上对PID参数进行离线寻优。参数整定方法采用多目标遗传算法（NSGA-II），以控制系统动态性能指标为目标函数，调整PID参数，从而提高控制效果。将所提出的算法通过在Matlab/Simulink中进行仿真验证，结果表明，经过离线参数优化后的蒸汽发生器液位控制系统在不同的工况中的超调量，调节时间上均优于原控制系统，具有更好的控制效果。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.
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Key words:
- Steam Generator /
- Identification /
- NSGA-II algorithm /
- PID parameter tuning
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图 1 蒸汽发生器液位控制系统 Simulink 模型
FLCV—满负荷阀门；LLCV—低负荷阀门；W_steam—蒸汽流量变化量扰动；PI(s)—流量控制器 PI；PID(s)—液位控制器 PID
Figure 1. Simulink Model of Steam Generator Liquid Level Control System
图 3 不同BP结构下的辨识结果对比
Figure 3. Comparison of Identification Results under Different BP Structures
表 1 不同辨识结构的辨识误差
Table 1. Identification Errors of Different Identification Structures
取值 均方根差 n=1 0.01 n=2 6.72×10−4 n=3 1.73×10−4 
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表 2 不同控制参数控制效果
Table 2. The Control Effect of Different Control Parameters
控制参数 超调量 调节时间/s 原系统 0.0898 170.2 A点 1.74×10−4 52.6 B点 0.02 17.4
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表 3 整定后的控制参数
Table 3. Control Parameters before and after Adjustment
控制参数 Kp1 Ki1 Kd1 Kp2 Ki2 原系统 7 0.1 0.45 0.51 0.49 A点 11.22 0.002 0.12 0.39 0.7 B点 18.62 0.005 0.97 0.99 0.45
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