冷冻水空调温湿度先进控制技术研究

发布时间：2018-06-17 06:50

本文选题：冷冻水空调 + 变频技术　；参考：《浙江大学》2016年博士论文

【摘要】：随着人们生活水平的日益提高,室内相对湿度对舒适度的影响越来越受到人们的关注。传统的冷冻水空调采用单一温度的冷冻水进行空气的降温除湿,冷冻水的温度往往大大低于空气的露点温度。为了达到合适的温湿度要求,需要用加热设备对过冷的空气进行再加热,从而导致能量的浪费。为了提高空调系统温湿度控制器的性能,减少系统能量消耗,必须对空调系统存在的多变量、非线性等特点做出相应的处理。本研究首先针对冷冻水空调冷冻水回路、室内空气回路的运行特点,深入研究非线性控制技术,对回路中的关键变量进行智能控制器设计。其次,在不增加额外硬件设备的基础上,对冷冻水空调的室内温湿度进行了基于先进控制理论的变频控制研究。实验结果表明,本研究提升了冷冻水空调关键回路中主要被控变量的控制性能,通过室内温湿度的同时控制很好地提升了室内环境的舒适性,并且达到了节能的目的。本文围绕冷冻水空调室内温湿度控制,主要开展了以下几个方面的工作：1.利用线性参数变化模型对蒸发器出口冷冻水的温度动态变化进行了建模,并且在该模型的基础上,设计了冷冻水温度的预测控制器。通过对制冷剂、冷冻水回路以及空气侧的换热特性进行研究,提出以冷冻水泵的频率作为系统的工况点,采用线性参数变化模型对蒸发器出口冷冻水的出水温度进行了建模。通过把系统在整个工况范围内的非线性表达成典型工况下线性模型的集成,系统的动态特性得到了很好的描述。基于线性参数变化模型,设计了预测控制器,通过多步优化得到了整个控制器的表达式。在实际的实验中,预测控制器在不同的工况点都达到了比传统控制器更加短的收敛时间和更好的抗干扰能力。2.针对空气处理单元(AHU, air handling unit)出口供风温度具有多变量、非线性的特点,利用神经网络具有在线学习的优点,设计了一种自适应神经网络控制器来对送风干球温度进行控制。从传热传质的机理上对影响室内空气干球温度的因素进行了分析。针对该过程具有多变量、非线性的特点,利用神经网络在处理非线性系统方面的优点,结合神经网络权值在线更新的功能,提出了一种基于自适应神经网络的空气干球温度控制器。该控制器不需要事先建立系统的动态模型,通过在线地学习反馈的运行数据,实时调整控制器的运行参数,从而获得更好的适应能力。该存在不确定的系统参数和外界环境干扰时,该控制器在收敛时间和超调量方面都获得了比传统控制器更优的效果。3.利用神经网络对冷冻水空调室内温湿度的耦合特性进行描述,并且结合预测控制器的策略,为冷冻水空调的室内温湿度设计了控制器。通过对机组的压缩机和空气处理单元中的供风风机加入合适的激励信号,利用神经网络首次建立了室内温湿度变化与压缩机转速和供风风机转速之间的关系。在得到系统的动态特性之后,借鉴预测控制的思想,设计了控制器的优化目标函数,通过求解该目标函数获得了控制器的最优解。在实际系统上进行目标值跟踪实验,相对于温湿度独立控制器,该控制器能够更好地处理温湿度之间的非线性特性,能够在更短的时间内跟踪温湿度的目标值。4.为了扩宽控制器的适用范围,解决控制器适用范围有限的不足,提出了基于自适应神经网络逆模型的室内温湿度控制器。直接利用动态系统的逆模型作为控制框架,同时通过对系统运行数据进行实时分析,在线更新控制器参数,从而使在能宽的工况范围内获得了满意的控制效果。具体实验也验证了该控制方法的可行性,目标值跟踪实验和扰动测试实验表明了该控制器比独立控制具有更加优秀的耦合特性处理能力和鲁棒性。本文针对冷冻水回路和室内控制回路的运行特点,结合先进控制技术,针对地为冷冻水出水温度和供风的干球温度设计了控制器,提升了回路的控制性能,优化了回路的运行。同时利用先进控制技术对冷冻水空调的室内温湿度进行了研究,在不增加额外空气处理设备的前提下,提升了温湿度控制的动态性能,达到了节能的目的。
[Abstract]:With the improvement of people's living standard, the influence of relative humidity on the comfort degree is getting more and more attention. The traditional frozen water air conditioning uses a single temperature freezing water for air cooling and dehumidification. The temperature of the frozen water is much lower than the dew point temperature of the air. In order to meet the appropriate temperature and humidity requirements, the temperature and humidity need to be added. Heat equipment reheating the supercooled air and resulting in the waste of energy. In order to improve the performance of the temperature and humidity controller of the air conditioning system and reduce the energy consumption of the system, it is necessary to deal with the multivariable and nonlinear characteristics of the air conditioning system. First, the refrigeration water loop and indoor air loop of the frozen water air conditioning system are first studied. The nonlinear control technology is deeply studied and the intelligent controller is designed for the key variables in the loop. Secondly, on the basis of no additional hardware equipment, the variable frequency control based on the advanced control theory is carried out on the indoor temperature and humidity of the frozen water air conditioning. The experimental results show that the frozen water air conditioning is improved by this study. The control performance of the main controlled variables in the key loop can improve the indoor environment comfortableness well and achieve the purpose of energy saving through the simultaneous control of indoor temperature and humidity. In this paper, the following aspects are mainly carried out around the temperature and humidity control in the refrigerated water air conditioning system: 1. using the linear parameter change model to the evaporator. The dynamic change of the temperature of the frozen water is modeled. On the basis of the model, the predictive controller of the freezing water temperature is designed. The heat transfer characteristics of the refrigerant, the frozen water loop and the air side are studied. The frequency of the frozen water pump is taken as the operating point of the system, and the linear parameter change model is used for the evaporation. The water outlet temperature of the frozen water is modeled. The dynamic characteristics of the system are well described by integrating the nonlinear expression of the system in the whole working condition into a linear model under the typical working condition. Based on the linear parameter change model, the predictive controller is designed and the expression of the whole controller is obtained by multi step optimization. In actual experiments, the predictive controller has achieved a shorter convergence time and better anti-interference ability than the traditional controller at different working conditions..2. is a multi variable, nonlinear special point for the air supply temperature of the air processing unit (AHU, air handling unit), and the design of the neural network has the advantage of online learning. An adaptive neural network controller is used to control the temperature of the air dry ball. From the mechanism of heat and mass transfer, the factors affecting the temperature of indoor air dry ball are analyzed. The characteristics of the process are multi variable and nonlinear, and the advantages of neural network are used to deal with the nonlinear system, and the weight value of the neural network is online. An air ball temperature controller based on adaptive neural network is proposed. The controller does not need to set up the dynamic model of the system in advance, and can adjust the operating parameters of the controller in real time by learning the running data of the feedback on-line, thus obtaining better adaptability. When the boundary environment is disturbed, the controller is better than the traditional controller in the time of convergence and overshoot..3. uses the neural network to describe the coupling characteristics of the indoor temperature and humidity in the frozen water air conditioner, and combines the strategy of the predictive controller to design the controller for the indoor temperature and humidity of the frozen water air conditioning. The wind fan in the compressor and air conditioning unit is added to the appropriate excitation signal. The relationship between the indoor temperature and humidity changes with the compressor speed and the speed of the fan is first established by neural network. After getting the dynamic characteristics of the system, the optimization objective function of the controller is designed for reference of the thought of predictive control. The optimal solution of the controller is obtained by solving the objective function. On the actual system, the target value tracking experiment is carried out on the actual system. Compared with the independent temperature and humidity controller, the controller can better deal with the nonlinear characteristics between temperature and humidity, and can track the value of temperature and humidity in a shorter time in order to broaden the scope of application of the controller and solve the problem. The limited scope of application of the controller is limited. An indoor temperature and humidity controller based on the adaptive neural network inverse model is proposed. The inverse model of the dynamic system is used directly as the control frame. At the same time, the parameters of the controller are updated on line through the real-time analysis of the operating data of the system, thus making it satisfactory in the wide range of working conditions. The feasibility of the control method is verified by the specific experiment. The target value tracking experiment and the disturbance test experiment show that the controller has a better coupling characteristic processing ability and robustness than the independent control. This paper is aimed at the running characteristics of the frozen water loop and the indoor control loop, combined with the advanced control technology. The controller is designed for the temperature of the frozen water and the dry ball temperature of the air supply. The control performance of the loop is improved and the operation of the loop is optimized. At the same time, the indoor temperature and humidity of the frozen water air conditioning is studied by using advanced control technology, and the dynamic performance of the temperature and humidity control is improved without increasing the extra air treatment equipment. It's the purpose of saving energy.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TP273;TB657.2

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