变风量空调控制系统在工程中的研究与应用

发布时间：2018-04-16 15:14

本文选题：空调系统 + VAV　；参考：《沈阳建筑大学》2015年硕士论文

【摘要】：随着人类社会的不断发展,能源危机逐渐成为人类所面临的重大威胁,能源的缺乏、环境的污染,都给人类的可持续发展带来了巨大影响。中央空调系统作为建筑环境中的主要能源消耗源,其节能技术研究对社会发展具有重要意义。变风量空调系统(Variable Air Volume System, VAV)是当前智能建筑中的主流系统。VAV系统具备了节能、灵活以及全空气系统的所有优点。但是,由于目前系统的控制方法大都采用PID控制,对于大滞后、大惯性、非线性系统来说,其控制精度不高。本文以VAV系统控制为切入点,在深入研究了VAV系统原理及工作特点的基础上,建立了空调系统环节的数学模型,结合实际工程项目,完成了VAV系统的静压控制方案设计,以及系统的部分软硬件设计,提出了提高VAV系统的控制效率的模糊神经网络控制方法,并参与了系统调试等工作,系统在工程中成功应用。首先,分析了VAV系统各环节的工作原理,特点及性能参数,通过物理、数学方法,建立了过滤器、表冷器、消声器、系统阻力等多个环节的数学模型。为分析和控制VAV系统提供了理论依据,对系统控制方法的设计具有重要意义。其次,以实际工程为研究背景,设计了VAV系统控制方式的方案,主要包括静压控制、温度再设控制、最小新风控制、空调机组联动控制等控制方案,提出了VAV送风压力优化控制策略—定静压与变静压结合再静压重设控制策略,克服了定静压系统不能实时改变,导致控制能量不能最佳匹配的缺点,从而优化了整个系统控制策略,提高了系统的动态和稳态性能指标,实现了VAV系统的优化控制。针对VAV系统的特点,为进一步优化控制效果,设计了模糊神经网络控制器,该控制器将模糊控制技术与神经网络控制技术有机结合,运用于整个静压系统的设计与优化,实现了模糊推理和自适应非线性控制,提高了系统鲁棒性,使控制效果明显提高。最后,运用Matlab仿真平台,设计了基于模糊神经网络的VAV静压控制仿真实验。仿真实验表明,本文所提出的控制算法,可以有效提高系统的控制精度,进而节能。
[Abstract]:With the continuous development of human society, the energy crisis has gradually become a major threat to mankind. The lack of energy and the pollution of the environment have brought great influence to the sustainable development of human beings.As the main source of energy consumption in the building environment, the research on energy saving technology of central air conditioning system is of great significance to social development.Variable Air Volume system (VAVV) is the mainstream system of intelligent building. VAV system has the advantages of energy saving, flexibility and all-air system.However, the current control methods of the system mostly use PID control, for the large lag, large inertia, nonlinear systems, its control accuracy is not high.This paper takes VAV system control as the breakthrough point, on the basis of deeply studying the principle and working characteristics of VAV system, establishes the mathematical model of air conditioning system link, and completes the static pressure control scheme design of VAV system combining with the actual project.A fuzzy neural network control method to improve the control efficiency of VAV system is proposed, and the system is successfully applied in engineering.Firstly, the working principle, characteristics and performance parameters of VAV system are analyzed. The mathematical models of filter, surface cooler, muffler and system resistance are established by physical and mathematical methods.It provides a theoretical basis for the analysis and control of VAV system, and is of great significance to the design of system control method.Secondly, taking the actual project as the research background, the control scheme of VAV system is designed, which mainly includes static pressure control, temperature re-setting control, minimum fresh air control, air conditioning unit linkage control and so on.The optimal control strategy of VAV air supply pressure is put forward, which combines static pressure with variable static pressure and restatic pressure reset control strategy. It overcomes the shortcoming that the static pressure system can not be changed in real time and the control energy can not be matched optimally.Thus, the control strategy of the whole system is optimized, the dynamic and steady performance index of the system is improved, and the optimal control of the VAV system is realized.According to the characteristics of VAV system, in order to further optimize the control effect, a fuzzy neural network controller is designed. The controller combines the fuzzy control technology with the neural network control technology, and is applied to the design and optimization of the whole static pressure system.The fuzzy reasoning and adaptive nonlinear control are realized, the robustness of the system is improved, and the control effect is improved obviously.Finally, the simulation experiment of VAV static pressure control based on fuzzy neural network is designed by using Matlab simulation platform.Simulation results show that the proposed control algorithm can effectively improve the control accuracy of the system and save energy.
【学位授予单位】：沈阳建筑大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TU831

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