Research On Optimal Schedule And Intelligent Control For City Water Distribution Systems

With the quickly increase of city population in our country, and with the gradually improvement of people life, the service of city water distribution systems are brought up higher and higher requests, the scale of city water distribution systems gradually expand and water distribution systems become much more complex. Due to importance and complexity of water distribution systems, it is important significance to ensure water distribution systems to safety work and effective management. In order to enhance economic benefits and society benefits of water distribution systems, it is necessary to improve production level and efficiency of water plants by using modern industry technology and advanced process control theory, and it is indispensable to realize water distribution systems optimal schedule, intelligence prediction, supervision and management by modern management theories, optimal methods automation and computer technology. In this dissertation pipe network prediction, optimal schedule, intelligence control and the steady switch of high power electromotor are deeply researched and discussed for water distribution systems.Water supply pipe network prediction is premise base of optimal schedule. As the foundation of establishing model, BP neural networks is selected to research and discuss the principles and mechanisms of neural networks water supply pipe network prediction, and the method is presented how to establish water supply time serial prediction model based on BP neural networks. On the ground of the history datum of node pressure, pipe flow, water charge of water distribution systems, water supply pipe network prediction model based on BP neural networks is respectively established, and node pressure, pipe flow, water charge in next period of time are predicted by their respective BP neural networks model. Making use artificial neural networks recognize principles of water supply is to select proper neural networks model to approach practical systems. Based on these history datum, once BP neural networks model recognize principles of water supply through study, water supply request next time would be predicted.Proceeding from view of overall optimum and minimum water cost for water distribution systems, mathematic model of optimum schedule is built for complex water distribution systems. Genetic algorithms for optimum schedule of complex water distribution systems are presented, then code regular, chromosome evaluation and genetic operation are deeply studied, furthermore solution steps for optimum schedule are given in detail. Matlab is adopted to write optimum control program based on genetic algorithms, and the program is applied to simulate production control for certain city water distribution systems. Simulation result shows that overall optimum solution is obtained when genetic algorithms are used to optimum schedule of complex water distribution systems, and that production by optimum schedule program contributes to economize water-supply cost.According to practical production characteristic of water supplying plant second grade stations, mathematic model is formulated about optimizing combination of pump units. On account of model optimal solution, the pump units optimizing combination genetic algorithm is presented, then code regular, chromosome evaluation and genetic operation are deeply studied and discussed. In terms of pump units condition of some water plant second grade stations, Simulation research is carried out about pump units combination optimization. It is indicated that optimizing scheduler of pump units with this mathematic model acquires much satisfying available values and great economic benefits.Aiming at characteristics of vvvf constant pressure water-supplying system such as multi-parameters, strong coupling, nonlinear, long time delay, the self-adaptive Fuzzy-PID controller based neural networks is designed. The controller can make online self-regulation of PID parameters, according to different working condition, proper parameters are selected to effectively adjust water pressure constant. It is especially discussed about system identification NN1 and self-adaptive NN2,furthermore solution steps is given out about Fuzzy-PID control algorithm based on BP neural networks, simulation program is written in Matlab environment to make simulation research on controller,, Simulation result and field application show that after the Fuzzy-PID control algorithm based on BP neuralnetworks being used, both the static characteristic of control system and dynamic characteristic of that are improved, system output datum accord with referenced model output datum.According to electrical equivalent circuit and vector graph of induction electromotor, discussion is made in theory about the problem ,that exists in the course of transformation from variable frequency to work frequency. Furthermore it is pointed out that whether the high-power electromotor can be switched from VF to WF lies on phase consistency between work frequency phase voltage and corresponding work frequency phase voltage. At the same time switch method are studied and discussed. Frequency and phase discriminator is adopted in the industry test to successfully complete the transformation from VF to WF. As the switch electrical current is small, non-impact switch of the high-power electromotor is realized. Furthermore it is also presented that PLL is integrated inside VWF, when the frequency of PLL input sine wave is equal to the VCO base frequency, PLL can eliminate initiative phase errors, thus PLL can rapidly lock frequency and phase of VWF output voltage, and keep phase consistency between WF voltage and corresponding VF output voltage.