Design Of Real-time Monitoring System For Municipal Water Supply Network And Optimization Of Monitoring Points

Since the beginning of twenty-first Century， many important cities of theworld are facing the crisis of water resource. China is one of the thirteen dry country. Percapita water resources in China is only1/4to the world. Water shortage of China amounts to60×108m3. With the continuous expansion of city， the traditional manual coordinationoperation is unable to meet the current demand of the water supply pipe network system.There is a profound significance to strengthen the remote data acquisition andmonitoring capacity of water supply network. In order to further improve the city waterquality and improve the operation efficiency of the water supply system， we should focus oncomputer monitor system and scientific arrangement of monitoring points.In view of the operational characteristics of water supply network management，we putsforward the design scheme of real-time monitoring system of city water supply network inthis paper. We develop the software of water supply network real-time monitoring system.Thecommunication network of the system is the GPRS.The remote terminal unit(RTU) isdeveloped by South China University of Technology. The monitoring platform of watersupply network real-time monitoring system is developed through KingScada. Realtime remote monitoring， data analysis， fault diagnosis， data query and other functions canbe achieved by the system.And we put forward a optimization model for pressure monitoring points of city watersupply network. The model combined with the influence of water pressure and the fuzzysimilarity matrix of water relationship. The model is solved by particle swarm optimizationalgorithm. The layout of optimization model is more excellent than random deployment. Weanalyzed a water supply network by the model. The model achieved the autooptimization arrangement for pressure monitoring points and made the monitoring range max.The model can monitor region of each pressure monitoring points. It provide reliable basisfor the monitoring points.In this paper， we compared the model to the fuzzy clustering. The model proposed inthis paper consider the topological structure of network. And it combined withthe pressure distribution and hydraulic fluctuation. We compared particle the swarmalgorithm and the genetic algorithm. The particle swarm algorithm is generally faster thanthe genetic algorithm to converge to the optimal solution. And the answer is accurate. Theparameter setting is more simple. Finally， we discussed the expansion properties for monitoring points optimization scheme in the city water supply pipe network.