| The energy-saving control of the pump unit of the building secondary water supply system is one of the key research directions of building energy saving.Optimizing the operation scheduling scheme of the pump set is the basic method for realizing energy saving of the building’s secondary water supply system.In the existing distributed pump set optimization scheduling scheme,the central monitoring station collects the operation data of each pump equipment and performs centralized optimization calculations,thereby achieving centralized monitoring and optimized operation of the entire secondary water supply system.The central monitoring station processes the global information in a centralized manner,the information processing load is large,and the system is not easy to expand.The new building intelligent platform based on a flat,non-central network structure abstracts the electrical equipment or building space in the building into an intelligent unit,each unit corresponds to a computing process node(Computing Process Node,CPN),and each CPN is based on the physical spatial relationship Connect into a CPN network.In the CPN network,each CPN only exchanges data with neighboring CPNs,and through mutual cooperation,completes global calculation and control tasks in a self-organized manner.The new building intelligent platform does not require a central monitoring station for centralized information processing.At the same time,the CPN network has good scalability and is an emerging direction of research on intelligent building control technology.This thesis is oriented to a new type of building intelligent platform,combined with the optimal dispatching of pump units in the building’s secondary water supply system,and carried out the following research work:(1)Using EPANET to build a secondary water supply pipeline network simulation system in a community building,taking the water demand and change multiplier as input,the simulation operation analysis of the secondary water supply system was carried out,and the water demand data of the main section of the pump outlet was obtained The water flow rate and energy consumption data of each pump provide a data source for the subsequent research on the optimal operation algorithm of the pump set.(2)Taking the water demand data of the main section obtained from the simulationoperation as the target flow rate,and by optimizing the operation flow rate of each pump,the pump set optimization model with the minimum total electrical power of the system as the goal is finally established.Based on the decentralized genetic algorithm,the optimal scheduling of the pump set is realized.In the optimization process,each CPN node has set up a read,write and interactive storage area.By reading the flow value and current electric power of the neighboring water pump,writing its own new flow parameters,current electric power and genetic algebra,to achieve a constrained relationship between neighbors Flow data and current electric power value interaction.Through iterative optimization,an optimized scheduling strategy with the total water demand as the constraint and the minimum total electric power as the goal is finally obtained.The feasibility and effectiveness of the decentralized genetic algorithm are verified by the distributed algorithm simulation platform.(3)Designed the hardware architecture of the experimental system and the control scheme of the pump equipment for the new building intelligent platform,built the experimental platform of the secondary water supply system of the building,and conducted the optimized operation experiment of the pump unit.The system results show that the scheduling scheme based on the decentralized genetic algorithm has a certain energy saving effect.At the same time,the new building intelligent platform is based on the neighbor interaction mechanism,which can efficiently and flexibly realize the cooperative and optimized operation of building equipment.Figure [42] Table [4] Reference [53]... |
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