Online adaptive control, diagnostics and intelligent operation of multizone heating ventilating and air conditioning systems

多区域供暖通风空调系统在线自适应控制、诊断和智能运行

• 批准号: 36380-2007
• 负责人: Zaheeruddin, Mohammed
• 金额: $ 2.04万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=362298
• 关键词: Online; adaptive; control; diagnostics; intelligent

Heating ventilating and air conditioning (HVAC) systems in modern buildings consist of a large number of interconnected sub-systems, components and controllers to operate them. In such large systems it is very difficult for the operators to optimize the system operation by trial and error or locate faulty components manually. Inefficient operation coupled with faulty components increase energy consumption, shorten equipment life, and degrade indoor environment in buildings. Since HVAC systems consume nearly one half of total energy used in modern buildings, it is important to develop online strategies for energy optimal operation and diagnosis in order to minimize operating costs.Monitoring and control of HVAC systems in modern buildings is currently handled by the state-of-the-art energy management control (EMC) systems. However, most EMC strategies consist of simple heuristic control functions for executing time-of-day building operation schedules. Similarly the diagnostic functions are limited to different alarm levels which are intended for alerting operators to initiate corrective action. Since alarms alone are not enough to detect and isolate component or operation faults. With this as the motivation, a comprehensive research program is proposed to develop online adaptive and intelligent strategies for system operation, control and diagnostics. The  overall objective is to integrate operation, control and diagnostics functions to achieve improved functionality and operational efficiency of overall HVAC system. Experiments will be conducted to verify the developed algorithms in a real building EMC system platform.The novelty of the proposed research program stems from the fact that the methodology is based on intelligent control techniques and the developed algorithms are expected to contribute to significant reduction in energy consumption and improved operational efficiency of overall HVAC system.

现代建筑中的暖通空调（HVAC）系统由大量相互连接的子系统、组件和控制器组成。在这样的大型系统中，操作员很难通过试错或手动定位故障部件来优化系统操作。低效率的操作加上故障部件增加了能源消耗，缩短了设备寿命，并降低了建筑物的室内环境。由于暖通空调系统消耗了近一半的总能源使用在现代建筑物中，它是重要的是开发在线策略的能源优化运行和诊断，以尽量减少运营成本。监测和控制的暖通空调系统在现代建筑物目前是由国家的最先进的能源管理控制（EMC）系统。然而，大多数EMC策略包括简单的启发式控制功能，用于执行一天中的建筑物操作时间表。类似地，诊断功能限于不同的警报级别，这些警报级别旨在提醒操作员启动纠正措施。因为单独的警报不足以检测和隔离组件或操作故障。以此为动机，提出了一个全面的研究计划，以开发在线自适应和智能的系统操作，控制和诊断策略。总体目标是集成操作、控制和诊断功能，以实现整个HVAC系统的改进功能和操作效率。将在真实的建筑EMC系统平台上进行实验，以验证所开发的算法，所提出的研究计划的新奇源于这样一个事实，即该方法是基于智能控制技术和所开发的算法，预计将有助于显着减少能源消耗和提高整个HVAC系统的运行效率。