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Research On Leakage Location Of Zonal Water Supply Network Based On Pressure Monitoring Point Optimization

Posted on:2022-07-03Degree:MasterType:Thesis
Country:ChinaCandidate:J Y LiuFull Text:PDF
GTID:2492306536492384Subject:Heating, Gas Supply, Ventilation and Air Conditioning Engineering
Abstract/Summary:PDF Full Text Request
At present,my country’s water supply pipe network has serious leakage,and the urban water supply system has serious leakage,which has not been effectively controlled for a long time.According to the latest statistical data,the leakage rate of the water supply pipe network in most areas of my country has not yet reached the national "Water Ten" requirements,so reducing the amount of water leakage in the water supply pipe network is urgent.Reducing the leakage rate of the pipe network can save manpower and material resources,provide protection for residents’ safe water use,save water resources,and reduce carbon emissions.In order to quickly and accurately locate the leak location and reduce the amount of water leakage,this paper proposes a water supply pipe network leak location method based on the optimization of pressure monitoring points,and applies this method to actual cases for verification.The results show that the method can be effective Locate the leakage of the water supply pipe network,and the calculation complexity is low.First of all,four common clustering algorithms are compared with the uniformity of the clustering results and calculation time as indicators,and it is found that the K-Means clustering algorithm is effective,and the method is used to find the location of the candidate pressure monitoring points.Furthermore,with the largest monitoring range and the smallest number of pressure monitoring points as the objective function,the Borg multi-objective optimization algorithm is used to determine the optimal layout of pressure monitoring points.Then,a two-step method of locating the leakage area and accurately locating the leakage area is adopted.According to the pressure monitoring point to the pressure change response degree of other nodes when the leakage occurs,the pressure monitoring partition is carried out,and the method of pattern recognition is used to locate the leakage initially.By changing the location of the leakage,the amount of leakage and the water consumption of the node,the data set of the pressure of the monitoring point and the position of the leakage is obtained and the leakage area prediction model is trained.The model analyzes the pressure data to obtain the probability of leakage in each zone,so as to locate the leakage to a certain pressure monitoring zone and narrow the scope of the leakage location.After determining the leakage zone,the method based on hydraulic model is used to locate the leakage.This research proposes a new leakage modeling method of adding a leakage node in the middle of the pipeline to locate the leakage to the pipeline.Taking each pipeline in the leakage zone as a decision variable,and minimizing the difference between the simulated pressure value and the real pressure value as the objective function,the genetic algorithm is used to optimize and determine the possible leakage location,which realizes the accurate positioning of the leakage.Finally,this leakage location method was applied to the real water supply pipe network model for verification.ten cases were selected,and the leakage area prediction model successfully predicted ten actual leakage areas;the positioning method based on the hydraulic model was used to determine ten leakage locations,the linear distance between the real leakage pipeline and the model leakage location pipeline is used as an indicator of the accuracy of the leakage location.The average distance of the ten solutions is 85.6m.The results show that the method can locate the leakage to the distance The area near the leak location shows that the leak location method proposed in this study is feasible and effective.
Keywords/Search Tags:water supply network, leakage location, pressure monitoring point, pattern recognition, hydraulic model
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