摘要
为使管网能够在压力分区后仍能同时满足典型日、最高日最高时和事故工况下关键控制点的最低压力需求,提出了一种基于粒子群优化的管网事故工况压力调控模型。首先,寻找每个压力分区的最不利节点和最不利管段作为模拟火灾事故点和故障管段;其次,以24 h的典型日工况下的泵阀调控方案作为初始条件,判断每个时段的调控方案是否能满足事故工况下的最低压力需求;最后,对于不满足的时段利用粒子群优化算法寻优,制定新的压力管理方案。以宁夏中卫市沙坡头区供水管网为例进行验证,结果显示该方法在事故工况下使管网弹性力指数从0.46上升到0.56,失效指数从0.47下降到0,供水保障效果明显改善。
In order to enable the water supply network to meet the minimum pressure requirements of control points under typical daily working conditions,high day high hour and accident conditions at the same time after pressure zone,a pressure control model for accident conditions based on particle swarm optimization is proposed.First,look for the most unfavorable node and the most unfavorable pipe section of each pressure zone as the simulated misfire point and fault pipe section.Secondly,use the pump valve control scheme under the typical daily working condition of 24 hours as the initial condition to judge the control of each period whether the plan can meet the minimum pressure requirement under accident conditions.Finally,for the unsatisfied period,the particle swarm optimization algorithm is used to re-formulate the optimal pressure management plan.Taking the water supply network in Shapotou district,Zhongwei city,Ningxia as an example,the results show that the method increases the elasticity index from 0.46 to 0.56 and the failure index from 0.47 to 0 under accident conditions,and the effect of water supply protection is significantly improved.
作者
孙广垠
张伟倩
雷晓辉
龙岩
徐秋红
王琦
SUN Guangyin;ZHANG Weiqian;LEI Xiaohui;LONG Yan;XU Qiuhong;WANG Qi(School of Energy and Environmental Engineering,Hebei University of Engineering,Handan 056000,China;Hebei Key Laboratory of Smart Water Conservancy,Hebei University of Engineering,Handan 056000,China;Zhuzhou SouthernValve Co.,Ltd.,Zhuzhou 412007,China;School of Civil and Transportation Engineering,Guangdong University oj Technology,Guangzhou 510006 China)
出处
《给水排水》
CSCD
北大核心
2022年第5期139-145,共7页
Water & Wastewater Engineering
基金
国家重点研发计划(2018YFC0408101)。
关键词
压力分区
事故工况
粒子群算法
泵阀调控
Pressure zone
Accident conditions
Particle swarm optimization
Pump valve control
