An Empirical Method for Estimating Thermal System Parameters Based on Operating Data in Smart Grids

被引:0
|
作者
Holland, Lee [1 ]
Karayaka, H. Bora [1 ]
Tanaka, Martin L. [1 ]
Ball, Aaron [1 ]
机构
[1] Western Carolina Univ, Dept Engn & Technol, Cullowhee, NC 28723 USA
来源
2014 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT) | 2014年
关键词
System Identification; Thermal Load Modeling; Load Regulation; Smart Grids;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
An experimental methodology was developed for online system identification of a thermal system or heated space. In this setting, the intelligent controller detects system parameters during normal operation and adapts its performance accordingly. The ultimate goal is to demonstrate that load leveling with demand side management can be used to reduce peak power consumption while maintaining residential room temperatures at a comfortable level. A prototype enclosure was built and equipped with a heater and thermal measuring equipment. Data was collected during a 17 hour temperature regulation experiment using a bang-bang controller similar to those commonly used for residential heating control. First and second order mathematical models were developed for thermal system identification. The mathematical models utilized the collected temperature data to estimate the net thermal resistance and capacitance using system identification techniques. Results showed the second order model to match the real system characteristics reasonably well. It was found that even for a small prototype enclosure, the estimated thermal parameters showed quite large values of thermal capacitance which can be a great asset for demand side management and control applications in a smart grid. The system identification method developed here is an important step toward the development of intelligent controllers.
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页数:5
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