The Research On Coordination Optimal Control For Hierarchical System Of Variable Air Volume Central Air Conditioning System

As an important part of the intelligent building, VAV central air conditioningsystem is of widespread concern due to its advantages of comfort, energy saving andflexibility. But in the engineering and controlling process, the actual operation of eachdevice may deviate from the optimal design, thus affecting the operation and energyefficiency of the overall system. Therefore, it’s of high significance to constructsteady-state model, search global optimal set point according to the actual situation, andminimize the energy consumption on the basis of indoor air quality and human comfort.For central air conditioning system composed of multiple subsystems, it is aneffective means to make integrated decision from the large-scale systems based on localcontrol optimization. The thesis focuses on the comprehensive needs of energy saving,indoor air quality and human comfort, to explore the overall solution to the system.In the research, the VAV air conditioning system is decomposed in hierarchicalstructure on the basis of large-scale system decomposition-coordination theory andoptimal control strategy, and the overall hardware and software platform of theexperimental system and the design of its subsystem function are introduced.The research was conducted from the perspectives of energy-saving strategies andcontrol algorithms. The subsystem dynamic model was constructed and the GPCalgorithm and Neural Network-PID control algorithm for local control unit wasdesigned. Herein, variable static pressure control strategy and demand controlventilation strategy were implemented based on the generalized predictive controlalgorithm and NN-PID control algorithm respectively. The experiments show that the algorithm with strong tracking and anti-jamming capability exhibits the considerablepotential for energy saving.With the outdoor meteorological parameters adopted to predict the air-conditioningload, ASHRAE correction coefficient method was proposed to predict the outdoorhourly temperature. Thereby the training data set was constructed. The Elman neuralnetwork and Grey-NN neural network prediction algorithm were designed, and theforecast results of air conditioning system dynamic load could be the basis for theobjective function and constraints of the global optimization.Steady-state optimization problem of VAV air conditioning system was studied,and the steady state model was established in the research. The global systemoptimization operating conditions model was constructed. An improved interbalancemethod (IBM) was designed to coordinate the global system, and the association couldbe equilibrium in the optimal point to ensure the convergence of coordination.Energy-saving optimization of the system control was implemented according to thesearched optimized value in each optimization cycle through the interaction between thetwo levels.The experimental results show that, using global optimization strategy can be agood solution to the central air-conditioning control and optimization of the globalsystem with large energy saving potential in winter conditions. And the strategy can beextended to a class of large-scale conditions process systems.