变风量空调系统递阶结构协调优化控制研究

发布时间：2018-03-07 12:01

本文选题：变风量　切入点：递阶结构　出处：《西安建筑科技大学》2013年博士论文　论文类型：学位论文

【摘要】：变风量中央空调系统作为智能建筑的重要组成部分，凭借其舒适、节能、灵活性等方面的优势被广泛关注。而现场工程及控制过程中系统的运行由于受到建筑物环境及空调负荷变化等因素的影响，使得各设备的运行偏离了所设计的最优工作点，从而影响系统整体的运行和节能。如何从系统全局出发，根据现场运行情况，构建系统稳态模型，搜索全局最优设定点，在满足室内空气品质和舒适性的前提下，最大程度地降低系统能耗，成为一个有意义的研究课题。对于中央空调这个由多个子系统组成的大系统，在局部控制优化的基础上，实现全局系统优化控制，将局部控制环节整合起来，从大系统的角度综合决策、协调优化是一种行之有效的手段。论文针对节能环保、室内空气品质及人体舒适度的综合需求，寻求系统全局层面的解决方案。论文基于大系统“分解-协调”理论与优化控制策略，在对智能建筑变风量(VAV)中央空调系统进行递阶结构分解的基础上，介绍实验系统的整体软硬件平台及各子系统的功能设计。分别从控制算法和节能策略两个角度进行研究。建立各子系统动态模型，并设计广义预测控制器及神经网络PID控制器用于底层子系统的控制。实现了基于广义预测控制算法的风系统变静压控制策略及基于NN-PID控制算法的需求控制通风策略。实验表明，算法具有较强跟踪性及抗干扰能力的同时体现出可观的节能潜力。引入室外气象参数用于空调负荷的预测，提出一种修正的ASHRAE系数法预测室外逐时温度，由此构建训练数据集。设计Elman神经网络及Grey-NN神经网络预测算法，对空调系统动态负荷的预测结果为全局优化目标函数及约束的确定提供依据。研究变风量空调大系统的稳态优化问题，建立系统稳态模型；构建全局系统优化运行工况模型；设计改进的关联平衡法(IBM)对全局系统进行协调优化，可在最优点处使关联达到平衡，且保证协调的收敛性；在设定的优化周期内，通过两级之间的交互，寻得各子系统的优化设定值，进而送至底层控制器，完成递阶优化控制。以冬季工况为例进行的实验结果表明，采用全局优化策略能够很好解决中央空调全局系统的控制和优化问题，具有一定的节能潜力，且可推广到一类大范围工况过程系统。
[Abstract]:As an important part of intelligent building, VAV central air conditioning system, with its comfort, energy saving, The advantages of flexibility and so on have been paid more and more attention. However, the operation of the system in the field engineering and control process is affected by the change of the building environment and the air conditioning load, which makes the operation of the equipment deviate from the optimal working point of the design. So as to affect the operation and energy saving of the whole system, how to set up the steady-state model of the system, search for the global optimal setting point, and satisfy the indoor air quality and comfortableness from the overall situation of the system, according to the field operation situation, and how to build the steady-state model of the system, It has become a meaningful research topic to minimize the energy consumption of the system. For the large system of central air conditioning, which is composed of multiple subsystems, on the basis of local control optimization, the global system optimization control is realized, the local control links are integrated, and the comprehensive decision is made from the angle of large system. Coordination and optimization is an effective method. Aiming at the comprehensive demand of energy saving and environmental protection, indoor air quality and human body comfort, the paper seeks a solution on the overall level of the system. Based on the "decomposition-coordination" theory and optimal control strategy of large scale system, this paper decomposes the hierarchical structure of VAVV central air conditioning system in intelligent building. The whole hardware and software platform of the experimental system and the function design of each subsystem are introduced. The dynamic models of each subsystem are established from two aspects: control algorithm and energy-saving strategy. The generalized predictive controller and neural network PID controller are designed for the control of the underlying subsystem. The variable static pressure control strategy of wind system based on generalized predictive control algorithm and the ventilation strategy based on NN-PID control algorithm are realized. The algorithm has strong tracking ability and anti-interference ability, and shows considerable energy saving potential at the same time. This paper introduces outdoor meteorological parameters to forecast air conditioning load, proposes a modified ASHRAE coefficient method to predict outdoor hourly temperature, and constructs training data set. Elman neural network and Grey-NN neural network prediction algorithm are designed. The prediction results of dynamic load of air conditioning system provide basis for the determination of global optimization objective function and constraints. The steady-state optimization problem of VAV large scale air conditioning system is studied, and the steady-state model of the system is established; the optimal operating condition model of the global system is constructed; the improved relational balance method is designed to coordinate and optimize the global system. The correlation can be balanced at the best point, and the convergence of coordination can be guaranteed. In the set optimization period, the optimal set value of each subsystem can be found through the interaction between the two levels, and then sent to the bottom controller to complete the hierarchical optimization control. The experimental results under winter conditions show that the global optimization strategy can solve the control and optimization problems of central air conditioning system well, and has a certain energy saving potential, and can be extended to a class of large-scale operating process systems.
【学位授予单位】：西安建筑科技大学
【学位级别】：博士
【学位授予年份】：2013
【分类号】：TU831.3

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