Research On Optimal Arrangement Of Pressure Monitoring Point And Pipe Bursts Warning Location Of Water Supply Pipeline Network Based On Swarm Intelligence Optimization Algorithm

In recent years,my country’s policy orientation in the field of leakage control of water supply network has promoted the rapid development of "Smart Water".In order to effectively improve the intelligent management level of the pipe network and reduce the leakage rate of the water supply network,the water affairs department urgently needs to establish and improve the hydraulic model of the urban water supply network,and rely on modeling technology to drive the optimization of the monitoring system and the detection of burst pipes research.In the above context,this research relies on a “Smart Water”transformation and upgrading project in a coastal town,focusing on the pipeline network modeling technology,the optimized arrangement of pressure monitoring points,and the burst warning location technology.Firstly,the study established a micro hydraulic model of the urban water supply network based on the EPANET platform,and ensured the accuracy of the hydraulic model through steps such as topology troubleshooting and model parameter verification.Secondly,aiming at the lack of theoretical guidance for the layout of pressure monitoring points in the water supply network,the optimal arrangement model of monitoring points was established by taking the maximization of monitoring range as the target and combining the water pressure correlation and water pressure sensitivity between nodes,and the pratical problem was transformed into a single-objective combination optimization problem.Two swarm intelligence optimization algorithms—Bat Algorithm(BA)and Particle Swarm Optimization(PSO)were applied in a water supply network of a town in southeastern coast to solve the model,and the optimal location of pressure monitoring points was realized in the example pipe network.The optimization results and performance of the two algorithms were compared in many aspects.Later,through the establishment of a monitoring effect evaluation system,it provided a clear quantitative standard for evaluating the pressure measurement point layout plan,found the best balance between monitoring accuracy and economic benefits,and the optimal pressure measurement point layout was finally determined based on the actual situation of the pipe network.Finally,after completing the optimized layout of the pressure monitoring points,the research established the burst warning and positioning models based on the pressure monitoring data.The pipe burst early warning model analyzes the pressure and pressure drop changes of the pipe network before and after the pipe burst,and calculates a reasonable confidence interval for the pressure and pressure drop fluctuations,so as to effectively screen abnormal data and give early warning of potential pipe burst risks.The burst tube positioning model takes the minimum square error value between the measured value and the simulated value at the time of the burst tube as the objective function,takes the burst tube node ID and the leakage coefficient K as the decision variables,and changes the leakage loss of the node.The coefficient K is used to simulate pipe burst accidents with different leakages,and the BA and PSO optimization models are used to obtain the position of the leakage and the amount of water leakage,and the accurate positioning of the pipe burst in the water supply network is realized under the working conditions of single node and double node leakage.