摘要
在极端水力荷载条件下,水流速度加快、流量激增,对大坝闸门施加巨大动水压力,导致闸门受力急剧波动并引发振动,使得其自振特性难以准确分析,影响流量控制的精度。因此,提出考虑极端水力荷载的大坝溢洪流量自控制方法。通过分析大坝实现溢洪流量控制的原理,确定通过控制闸门开度实现溢洪流量控制。利用流固耦合自振特性方程,分析极端水力荷载下大坝闸门的自振特性,为控制提供理论依据。根据分析结果建立PID模糊控制器,将水位误差作为输入,输出阀门开度控制结果。为了提高流量控制实时性,利用BP神经网络展开反馈控制,根据水位误差的变化情况实时调整PID控制参数,实现大坝溢洪流量自控制。实验结果表明,所提方法水位监测的精准度显著提升,流量控制达到了高度的精确性,并且整个控制过程展现出极高的实时响应能力。
Under extreme hydraulic loading conditions,the water velocity accelerates and the flow rate surges,which exerts huge dynamic water pressure on the dam gates,leading to sharp fluctuations in the gate force and triggering vibration,which makes it difficult to analyze its self-oscillating characteristics accurately and affects the accuracy of flow control.Therefore,the self-control method of dam spillway flow considering extreme hydraulic load is proposed.By analyzing the principle of dam spillway flow control,it is determined that the spillway flow control is realized by controlling the gate opening.The self-oscillation characteristic of the dam gate under extreme hydraulic load is analyzed by using the fluid-solid coupling self-oscillation characteristic equation,which provides a theoretical basis for the control.According to the analysis,a PID fuzzy controller is established,taking the water level error as input and outputting the valve opening control results.In order to improve the real-time flow control,a BP neural network is used to carry out feedback control,and the PID control parameters are adjusted in real-time according to the change of water level error,so as to realize the self-control of the dam spillway flow.The experimental results show that the proposed method significantly improves the accuracy of water level monitoring,the flow control achieves a high degree of accuracy,and the whole control process shows a very high real-time response capability.
作者
刘光
叶强
LIU Guang;YE Qiang(Liaocheng Hydrological Center,Liaocheng Shandong 252200,China;Shanxi Agricultural University,Liaocheng Shandong 252200,China)
出处
《计算机仿真》
2025年第4期502-506,共5页
Computer Simulation
基金
山东省科技项目(2019ZDJS02006)。
关键词
水力荷载
流固耦合自振特性方程
模糊控制器
神经网络
大坝溢洪流量控制
Hydraulic loading
Fluid-solid coupled self-oscillating characteristic equations
Fuzzy controller
Neural network
Dam spillway flow control
