Zhejiang University Press
Hangzhou
Zhejiang University Press, co-published with Springer
11582
1673-565X
1862-1775
Journal of Zhejiang University-SCIENCE A
Applied Physics & Engineering
J. Zhejiang Univ. Sci. A
Engineering
Mechanical Engineering
Civil Engineering
Classical and Continuum Physics
Industrial Chemistry/Chemical Engineering
16
16
12
4
4
7
2015
4
11
2015
4
2015
Zhejiang University and Springer-Verlag Berlin Heidelberg
2015
13
10.1631/jzus.A1400137
5
Global optimal control of variable air volume air-conditioning system with iterative learning: an experimental case study
基于迭代学习的变风量空调系统全局优化控制实验研究
302
315
2015
9
3
2014
5
11
2014
10
29
2015
10
1
Zhejiang University and Springer-Verlag Berlin Heidelberg
2015
Qing-long
Meng
mql19@163.com
Xiu-ying
Yan
xjdyxy1219@163.com
Qing-chang
Ren
0000 0000 9225 5078
grid.440661.1
School of Environmental Science and Engineering
Chang’an University
Xi’an
710054
China
0000 0000 9796 4826
grid.440704.3
School of Information and Control Engineering
Xi’an University of Architecture & Technology
Xi’an
710055
China
Abstract
The air-conditioning system in a large commercial or high-rise building is a complex multi-variable system influenced by many factors. The energy saving potential from the optimal operation and control of heating, ventilating, and air-conditioning (HVAC) systems can be large, even when they are properly designed. The ultimate goal of optimization is to use the minimum amount of energy needed to improve system efficiency while meeting comfort requirements. In this study, a multi-zone variable air volume (VAV) and variable water volume (VWV) air-conditioning system is developed. The steady state modes and dynamic models of the HVAC subsystems are constructed. Optimal control based on large scale system theory for system-level energy-saving of HVAC is introduced. Control strategies such as proportional-integral-derivative (PID) controller (gearshift integral PID and self-tuning PID) and iterative learning control (ILC) are studied in the platform to improve the dynamic characteristics. The system performance is improved. An 18.2% energy saving is achieved with the integration of ILC and sequential quadratic programming based on a steady-state hierarchical optimization control scheme.
摘要
目的
采用实验方法研究变风量全局优化问题, 利用迭代学习控制策略优化动态控制性能, 获得变风量系统在系统层次的最优。
创新点
1. 采用全新的兼有变风量和变水量功能的实验平台; 2. 引入递阶优化控制理论, 建立变风量系统的动态和稳态模型; 3. 采用先进控制策略, 如自校正比例积分微分 (PID) 控制和迭代学习控制等。
方法
1. 将系统进行分解 (图 4), 并建立系统稳态模型 (公式 6–11)、 动态模型 (公式 12–15) 和能耗模型 (公式 16); 2. 在此基础上采用变速积分 PID、 自校正 PID 和迭代学习控制对系统底层进行动态控制, 在系统整体优化中引入迭代学习。
结论
1. 先进控制策略的引入有利于优化变风量系统动态控制过程; 2. 采用基于迭代学习的优化方法, 可使系统节能约 18.2%。
Key words
Air-conditioning system
Large scale systems
Iterative learning control (ILC)
Global optimization
关键词
空调
大系统
迭代学习控制
全局优化
Project supported by the National Natural Science Foundation of China (No. 51208059), the Special Natural Science Research Project of Shaanxi Education Bureau (No. 2013JK1052), and the Special Fund for Basic Scientific Research of Central Colleges, Chang’an University (No. 0009-2014G1291074), China
ORCID: Qing-long MENG,
http://orcid.org/0000-0002-3976-2331
; Xiu-ying YAN,
http://orcid.org/0000-0001-9949-8870
ftp_PUB_17-03-24_05-06-31.zip11582-2015-Article-13.pdfPDF1.4