粒子群优化PID在变风量空调系统中的应用

针对在变风量(VAV)空调系统中,传统PID控制自适应能力差、对非线性系统控制效果不佳的问题,提出了一种基于粒子群优化(PSO)算法整定PID参数的方法。通过选取被控对象模型(空调房间模型),在目标空间中寻找最优PID控制参数集,并选取被控系统的误差绝对值乘时间积分(ITAE)指标作为粒子群算法的目标函数。通过设计优化程序,得到了系统仿真图。与传统Ziegler-Nichols(Z-N)算法整定PID结果相比,系统在工况切换时超调量降低、调节时间缩短。为了避免粒子群算法陷入局部极值,对算法进行了加权。在系统中的应用效果表明,与基本PSO算法相比,加权后的PSO算法在系统工况切换时,收敛速度变慢、系统偏差量减小、调节时间缩短。经仿真分析得出,加权后的PSO算法能有效地降低PID控制器的超调量。该优化算法应用于具有多种变化特性的VAV空调系统中,提高了系统的控制性能。     

预估模糊免疫PID在房间温度控制中的应用

针对空调房间温度对象的大滞后、大惯性、多干扰的特性,设计了具有史密斯(Smith)预估功能的模糊免疫PID控制器.该控制器采用Smith预估和免疫PID控制相结合的结构,借鉴免疫算法的生物免疫反馈机理进行PID参数的在线自适应调整;用模糊控制器来逼近免疫PID的非线性函数,并用遗传算法优化它的隶属度函数.对该控制方案进行系统仿真结果表明:该方法抗扰能力强、调节速度快、超调量小,可用于精确的控制房间温度.     

压水堆核电站稳压器压力控制系统仿真研究

研究PID控制器参数优化问题,针对稳压器压力控制系统具有复杂非线性、时变性特点,引起系统的输出品质特性较差,超调量大,调节时间长,上升时间长,控制精度差等。传统PID的控制参数难以精确整定,且依赖于对象的精确数学模型。为了提高PID控制精度,减小超调量、调节时间和上升时间,提出用单神经元的神经网络来优化PID控制器参数的方法。通过单神经元的自学习和自适应能力,获得最优控制性能的PID控制参数。仿真结果表明,单神经元神经网络的PID控制方法与传统的PID控制方法相比,系统响应速度更快,超调量更小,为优化控制系统提供了参考。     

蚁群算法在PID控制器参数优化中的应用研究

PID参数优化一直是控制工程领域研究的热点,针对提高系统的稳定性和响应特性,传统的PID控制参数多采用试验的方式进行优化,往往费时而且难以达到较好的控制效果.为了解决控制参数优化,改善系统性能,提出一种新型的蚁群算法的PID参数优化策略,将蚁群算法能快速稳定找到最优参数解的特点与PID精确调节的特点有机结合起来,在控制...     

控制系统出现饱和时改善动态品质的一种有效方法

在工业生产过程的实时控制中,PID控制是应用最广泛的一种控制规律。目前由计算机取代常规的 PID 调节装置而构成的直接数字控制系统(DDC)已得到广泛地应用。但是 DDC 系统和一般控制系统一样‘其调节品质也受到系统饱和的影响。本文提出了当数字 PID 控制系统中出现饱和现象时,加入缓冲滤波器来改善系统品质的设想,并提出了缓冲滤波器参数的两种计数方法。通过计算机仿真,确认了设想的有效性和计算方法的实用价值。     

分时二次优化控制的空调温湿度控制系统

空调温湿度自控系统是一个多输入多输出、典型强耦合的非线性时变系统,具有鲜明的纯滞后、大惯性特点.针对此特点,结合在制药行业空调控制的具体应用、预估模型和传统PID,提出了一种新型空调控制模式-分时二次优化控制方法.此方法运用前面机理建模方法得出的系统模型,使用现场实验数据确定系统模型参数,通过二次优化得到不同的控制参数...     

浅谈DDC控制器在楼宇自控空调系统的调试

暖通空调系统中的控制系统采用方便、灵活的直接数字化控制系统,不仅确保空调系统能够更加有效率地运转、提供更加舒适的环境,而且还可以使空调系统始终处于最佳的节能状态。本文根据丰富的项目调试经验,对楼宇智能化控制系统中DDC控制器的调试进行了系统性阐述,希望同行可以参考借鉴。     

神经PID在地源热泵空调系统中的应用

针对地源热泵空调系统被控对象具有大滞后、慢时变、非线性特点及不确定干扰因素多的实际情况,结合常规PID和神经网络的优点,提出了一种基于BP神经网络的PID控制器设计方法,并应用于该空调系统中。仿真结果表明,该方法可以显著改善系统的动态性能和控制精度,实现了PID控制参数的在线动态调整,取得了良好的控制效果。     

基于神经网络PID电子负载控制系统设计

以电子负载为研究对象,针对其控制系统复杂的非线性和时变性,常规的PID控制方法存在着精度低,自适应能力不强等缺点,以定点32位DSPTMS320F2812作为控制芯片,结合神经网络和常规PID设计了一种神经网络PID控制器.仿真结果表明,通过神经网络PID控制器,能够在线调整PID控制参数,使系统具有更小的超调量和更短的调节时间,而且系统的精度和稳定性也得到大大的提高.神经网络PID控制器是一种动态特性和静态特性良好的控制器,在电子负载控制实验中可以获得满意的控制效果.     

变新风量的最小焓差控制

基于焓值理论,得出影响焓差的关键因素是相对湿度和干球温度。将夏季工况下测试房间的空气参数绘入焓湿图中,依据上述理论分析不同空气状态下的焓值控制参数。利用最小焓差控制与新风量调节过程相结合的方法,使进入空气处理机组前的空气更加接近舒适区域并且减小表冷器的换热量。测试表明,变新风量最小焓差PID控制的效果优于定新风量PID控制,并且减少了表冷器换热量。在舒适性方面,测试房间的空气状态参数也稳定在焓湿图的舒适区域内。     

Dueling-DQN在空调节能控制中的应用

针对电信机房空调运行耗电量大,空调自动控制系统设计困难的问题,提出了一种规则约束和Dueling-DQN算法相结合的空调节能控制方法.该方法能根据不同机房环境自适应学习建模,在保证机房室内温度在规定范围的前提下,节省空调耗电量.同时针对实际机房应用场景,设计节能控制算法中的状态,动作和奖励函数,并采用深度强化学习算法Dueling-DQN提高模型表达能力和学习效率.在电信机房实际验证结果表明:该控制方法与空调默认设定参数运行相比节能18.3％,并可以很方便推广到不同环境场景的机房环境中,为电信机房节能减排提供解决方案.     

Integrated operation planning: Intelligent system approach for HVAC processes

This paper describes the construction of an Intelligent System for Operation Planning (ISOP) in heating, ventilating, and air conditioning (HVAC) processes. The system contains important expertise, qualitative reasoning, and quantitative computation. It is used to assist or train operators to achieve better operation in HVAC systems. Expertise about operation planning is expressed as air enthalpy, and moisture conditions and air supply are considered as dynamic parameters. Therefore, it provides a real-time integrated operation planning method in HVAC processes. It offers better energy conservation, comfort and indoor air quality than other methods being currently used. ISOP consists of two levels of frames. The first level classifies HVAC systems by qualitatively reasoning the system structure information, and activates the subframe. In the second level, 16 frames that correspond to the HVAC system structure, accomplish indoor comfort setting, supply air parameter estimations, air enthalpy, and misture evaluation, and then recommend optimal operation conditions. An integrated distributed intelligent system framework is introduced to integrate qualitative reasoning and quantitative computation.     

基于STM32F107的暖通空调混合通风自适应控制系统设计

针对暖通空调混合通风自适应控制系统受到室内空气环境的影响,导致暖通空调的通风控制效果变差,增加了空调能耗的问题,提出了基于STM32F107的暖通空调混合通风自适应控制系统设计。在STM32F107控制平台上,通过暖通空调混合通风自适应控制电源柜设计和暖通空调混合通风自适应控制器设计,完成了系统的硬件设计,结合暖通空调混合通风换气量的计算和暖通空调混合通风控制算法设计,完成了系统的软件设计,实现了暖通空调混合通风的自适应控制。测试结果表明,基于STM32F107的暖通空调混合通风自适应控制系统不仅可以控制室内的温度环境,还可以控制室内的湿度环境,有效提高了暖通空调混合通风的控制效果。     

Comfort-based fuzzy control optimization for energy conservation in HVAC systems

The work presented herein illustrates the use of computational intelligence and optimization approaches for improving the fuzzy controller׳s performance in architectural heating, ventilation, and air conditioning system (HVAC). The primary purpose of the performed research is to find a method to moderate the energy use without compromising the comforts of the inhabitants. The control design used to meet this purpose includes the predicted mean vote (PMV) and predicted percentage dissatisfied (PPD) indices. The software of choice for evaluating PMV and PPD is EnergyPlus. Whereas, for the fuzzy controller and the evolutionary optimization framework, the co-simulation tool with building controls virtual test bed (BCVTB) is used in conjunction with Simulink. The ensuing comparison between EnergyPlus׳s thermal control of HVAC and our fuzzy approach is the outcome of the present research.     

Effects of dead-band and set-point settings of on/off controllers on the energy consumption and equipment switching frequency of a residential HVAC system

Residential heating, ventilation and air conditioning (HVAC) systems generally employ simple on/off controllers to regulate the temperature of water and air in different subsystems. Selection of set-point of a controlled process and dead-band of controller affects the process regulation, energy consumption and actuator switching frequency. This article presents a calibrated model of a residential HVAC system. Temperature of two zones and buffer tank (BT) is regulated using on/off controllers. Non optimum controller settings result in poor regulation, higher energy consumption and higher equipment wear. The purpose of this article is to find optimum set-point and dead-band settings for on/off controllers in order to improve temperature regulation, reduce energy consumption and decrease equipment wear by reducing the switching frequency of HVAC equipment without scarifying thermal comfort of occupants.     

Deep learning forecasting for electric demand applications of cooling systems in buildings

This paper presents the application of a deep learning based model for the short-term forecasting of the electric demand in a heating, ventilation, and air conditioning system (HVAC) for the demand response programs of utility companies. The deep learning model is applied through two different approaches comparing their merits. The approaches consist of: (i) a monolithic approach that applies a single large model to forecast the target variables, and (ii) a sequential approach that consists of multiple deep learning models coupled together each targeting a specific energy load within the HVAC system. The model accuracy of both approaches is explored over two case studies applied to the same institutional building; however, the case studies differ in their data source. The first case study uses synthetic data obtained from an eQuest simulation, while the second case study uses measurement data obtained from the building automation system. Results show that the difference in forecasting error of these approaches is negligible; however, the monolithic approach required the least amount of calibration time. Next, this paper explores the application of off-site weather data applied to a building model calibrated with on-site data. The experiments demonstrated that the off-site weather data can be applied with a slight reduction in forecasting performance.     

恒温恒湿空调主/备机控制系统的设计

针对在众多的恒温恒湿空调机组的用户中,经常要求主/备机控制系统满足程控交换机房、计算机房等设备环境的长期一致稳定性,以有效地保证这些设备的正常运行问题,介绍了恒温恒湿空调机组主/备机控制系统的功能特点。以西门子PLCS7-200作为恒温恒湿空调机组的电脑控制器,分别提出了3种解决方案,着重讨论了其控制原理、设计方法及实现可行性。其中,利用PLCS7-200本身内部的PPI通信功能实现主/备机控制的方法,实际应用效果稳定、可靠,具有较强的实用性。     

DCS在核电站通风系统中的应用

以巴基斯坦恰希玛核电站二期工程为背景,介绍了集散式控制系统（DCS）在核电站通风系统中的应用。重点介绍了集散式控制系统的硬件设计、符合人因工程的人机接口画面设计及控制逻辑设计,并描述了系统的电磁兼容性设计措施等,以保证集散式控制系统在核电站非安全级系统中的成功应用。最后还提出了当前国产化DCS系统应用于核电项目时存在的突出问题及其改进建议。     

Normalized decoupling control for high-dimensional MIMO processes for application in room temperature control HVAC systems

A novel decoupling control system design for high-dimensional multi-input, multi-output (MIMO) processes is presented. Based on the Relative Normalized Gain Array (RNGA), an Equivalent Transfer Function (ETF) for each element in the transfer function matrix was derived for the closed-loop control system and was used to approximate the inverse of the process transfer function matrix. The decoupler could be easily determined with each element in the First-Order-plus-Time-Delay (FOPTD) form and resulted in a stable, proper and causal decoupled matrix. A PI/PID controller could then be designed to meet the performance objectives. The main advantage of this method was its simplicity; it did not require extensive calculation effort. To demonstrate its effectiveness, the decoupling control strategy was applied to control the temperatures of four neighboring rooms in an HVAC process.     

Smart grid coordination in building HVAC systems: EMPC and the impact of forecasting

Energy consumption by heating, ventilation, and air conditioning (HVAC) systems exhibits a clear correlation with electricity prices. The method of economic model predictive control (EMPC) can be used in conjunction with thermal energy storage (TES) to time-shift power consumption away from periods of high demand to periods of low energy cost. Dynamic electricity pricing and weather condition forecasts can be readily incorporated within this methodology. Unfortunately, the receding horizon nature of this control strategy makes it very susceptible to the quality of the forecasts used. To this end, the development and implementation of several forecasting methods will be discussed. Finally, the EMPC performance of these methods will be assessed on a simple building example using active TES.