摘要
冷水机组是建筑暖通系统中供冷/供热的核心设备,其运行工况复杂多变,且易受环境波动影响。对此,该文提出了一种基于多模型自适应预测控制(multiple model adaptive predictive control,MMAPC)的冷水机组智能调控策略。首先,根据热力学机理分析建立了冷水机组的机理模型;然后,基于模型预测控制理论,结合自适应加权的控制变量融合方法,设计了冷水机组的多模型自适应预测控制策略;最后,为方便多模型自适应控制策略的部署与测试,研发了可实现数据解析及自主决策的冷水机组调控实验平台。在三种典型工况的对比实验中,所提出多模型自适应预测控制策略比传统控制的平均跟踪误差降低了70%。
[Objective]The heating,ventilation,and air conditioning(HVAC)system is a major energy consumer in buildings,with the chiller—the core component of the system—playing a vital role in meeting cooling demands by carrying heat.Therefore,flexible demand-based regulation of the chiller is essential to improve building thermal comfort and reduce energy consumption.As a nonlinear,highly coupled,and dynamic system,the chiller exhibits varying system characteristics under different operating points and environmental conditions.This necessitates a control strategy capable of adapting to diverse operating scenarios.[Methods]To address the challenge of multicondition chiller regulation in HVAC systems,an intelligent regulation method based on multiple model adaptive predictive control(MMAPC)was proposed.To analyze the dynamic characteristics of chiller under different operating conditions,its working mechanism was examined using thermodynamic theory,and a chiller control model suitable for real-time operations was established.Based on the influence of environmental factors,three representative operating conditions were identified and classified.For each condition,an incremental model predictive controller was designed using the mechanism-based model.These controllers were integrated through an adaptive weighted control variable fusion approach to form the overall MMAPC strategy.To evaluate the proposed approach,a real-time experimental platform was developed,comprising an intelligent chiller regulation unit,a supervisory computer with a large display screen,and several underlying control devices.The platform supports flexible communication configuration,high-volume data processing,and the deployment of various intelligent algorithms.Comparative experiments between single MPC control and the proposed MMAPC strategy were conducted on this platform.[Results]Experimental results showed that the proposed MMAPC approach reduced the average tracking error by 70%compared with single MPC control.Additionally,it decreased the average overshoot between different operating conditions by approximately 75%and reduced the average standard deviation of the compressor valve opening by around 91%.These results demonstrated the feasibility and effectiveness of the proposed control strategy in achieving accurate chiller outlet temperature tracking while maintaining HVAC system stability.The developed experimental platform successfully enabled real-time data acquisition,strategy computation,and command issuance,while visually displaying system status on the large screen.[Conclusions]The intelligent experimental platform effectively supports real-time strategy verification and provides a practical foundation for teaching and research.The MMAPC strategy demonstrates excellent performance in multicondition chiller regulation and show strong potential for solving tracking control problems in dynamic,time-varying systems.This method lays the groundwork for deploying chiller operation optimization algorithms and contributes to energy-saving and emission-reduction goals under stable HVAC operation.
作者
杨旭
高仕航
李擎
张笑菲
高晶晶
崔家瑞
YANG Xu;GAO Shihang;LI Qing;ZHANG Xiaofei;GAO Jingjing;CUI Jiarui(School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education,University of Science and Technology Beijing,Beijing 100083,China)
出处
《实验技术与管理》
北大核心
2025年第10期12-21,共10页
Experimental Technology and Management
基金
国家自然科学基金项目(62373012)
教育部自动化类专业教学指导委员会专业教育教学改革研究课题(2024031,2024005)
北京科技大学优秀青年团队培育项目(FRF-EYIT-23-06)。
关键词
冷水机组
多模型自适应控制
模型预测控制
多工况
实验平台
chillers
multiple model adaptive control
model predictive control
multiple operating conditions
experimental platform
