【作者】 蒋红梅；

【导师】 任庆昌；

【作者基本信息】 西安建筑科技大学 ， 智能建筑环境技术， 2013， 博士

【摘要】 中央空调是现代建筑中的能耗大户，其耗能占整个建筑能耗的50%-70%。空调系统在设计时通常采用的是最不利工况设计，一般是按照空调系统最大的负荷来进行设计的。但实际运行时，空调系统90%以上的时间都是处于部分负荷状态下的，空调系统对于负荷的处理有很大的冗余，而且在实际的空气调节中也有很大的灵活性。变风量（Variable Air Volume，VAV）空调系统是一种通过调节风量来满足室内负荷变化及舒适性要求的全空气调节系统，由于其无凝结水害、设计系统灵活、高效节能的优点得到了广泛应用。然而，由于变风量空调系统具有非线性、大滞后、耦合性强、多变量、多扰动等特点，传统的控制方式难以适应其控制要求，使得变风量空调系统的节能性、舒适性得不到充分体现。如何通过最优化控制，使空调系统在满足环境舒适性的同时，能稳定的运行，并最大限度地减少系统能耗，就成为研究的重点。由于变风量空调系统设备较多，因此发生故障的频率也相对比较高。如果变风量空调系统中存在故障，会直接影响系统的能耗，导致系统能耗增加，并且会影响空调室内的舒适性。对于设备来说，会增加其损耗和减少其使用寿命。变风量空调系统应运行在无故障状态下，因此对于故障状态的检测就具有重要的现实意义。对与防止运行事故的发生，提高空调系统设备的有效利用时间，延长空调系统的使用寿命都具有非常良好的效果。本文通过改进的神经网络方法建立了变风量空调系统负荷模型，通过负荷的预测对变风量空调系统的能耗进行监测，并与实际的能耗检测值进行比较，利用统计学方法进行系统故障检测，能够在变风量空调系统运行过程中进行故障状态提示，确保变风量空调系统运行在无故障状态下。采用了一种基于协调的递阶优化控制，根据变风空调系统的工作原理对变风量空调大系统进行合理的分解，并通过实验对变风量空调大系统进行稳态建模，得到其稳态大系统模型。提出了其目标优化方法，以变风量空调系统舒适性和节能性为优化目标为各个控制器确立优化设定值，实现变风量空调系统的优化与节能控制。根据变风量空调系统主要部件的模型、能量平衡方程以及部件的物理限制定义了全局协调优化的目标函数和约束条件，实时优化系统各动态参数，通过寻找最优的操作条件，确定最佳工作点。并针对不同的控制回路采用不同自适应控制策略对各子系统进行稳定控制，使系统的控制参数始终维持在设定值附近。并对系统进行合理的设计、设备选型、软件选取和优化算法的实施，开发了变风量空调系统优化的计算机控制系统，对实际的操作提供了有指导意义的根据。仿真和实验研究结果表明该优化方法不仅能保证系统的舒适性而且能显著地降低系统能耗。更多还原

【Abstract】 Central air-conditioning is the large energy consumption of modern construction, itis almost50%~70%percent of the whole building energy consumption.Usually thedesign of air-conditioning system is on the most unfavorable condition, generally isaccording the maximum load. But actually,air-conditioning system always work in partload.It can be90%time. The ability of air treatment equipment has a lot of rich.Therefore, it adjustment also has a lot of flexibility. VAV (Variable AirVolume)air-conditioning system is a full air conditioning system. It meet the change ofload and comfort requirements by adjust the air volume. Because of its no condensationwater disasters, design system agile, high efficiency and energy saving.It has a widerange of applications. However, the VAV air-conditioning system has nonlinear, delay,strong coupling, multivariable and disturbance characteristics, it is difficult to adapt tothe control requirements by traditional control mode.This makes the energyconservation and comfort in VAV system not fully reflected. Through the optimizationcontrol, how to make the air-conditioning system in comfort, stable operation, andminimize the system energy consumption, will become the focus of research.Because the air conditioning system have a lot of equipments, fault frequencybecome higher. Fault will increase energy consumption and reduce indoor comfort. Atthe same time, it will increase the equipment loss, affect the service life. So make surethe VAV system is running in trouble-free state has very important practical significance.It has the good effect to prevent accidents, improve equipment effective use and prolongits service life.This article use the improved neural network to establish load model and obtain theenergy consumption. Then compared the forecast energy consumption and the actual consumption values in operation process. Fault state use statistical methods for faultdetection. To ensure the VAV system is running in trouble-free state. The system use acoordination control based on global hierarchical optimization. Decomposition andestablish the system static model according to the working principle of VAV system andthrough experiment. Puts forward the multi-objective optimization of VAV system. Thisoptimize can established optimization setting values for each controller. The goal ofoptimization control is more comfort and less energy conservation. According to themain parts of system model, the energy balance equation and components of thephysical limit, define the global energy saving optimization objective function andconstraint conditions. The optimization algorithm find optimal operation value in thesystem. Using different adaptive control strategy of each subsystem, make controlparameters always near the set point. Through system design, device selection, softwareplan and optimization strategy, we developed the computer control of VAV systemoptimization. It can provide directions in actual operation. The simulation andexperiment results show that the optimization method can not only ensure the comfortof the system and can significantly reduce the system energy consumption.更多还原