Data-driven Predictive Control of Micro Gas Turbine Combined Cooling Heating and Power system

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• 作者：Xiao Wu^* ; ^{wux@seu.edu.cn} ; Jiong Shen^* ; ^{shenj@seu.edu.cn} ; Yiguo Li^* ; ^{lyg@seu.edu.cn} ; Junli Zhang^* ; ^{230079407@seu.edu.cn} ; Kwang Y. Lee^** ; ^{Kwang_Y_Lee@baylor.edu}
• 关键词：Micro gas turbine-based cooling ; heating ; power system ; predictive control ; data-driven predictive control ; subspace identification
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：27
• 页码：419-424
• 全文大小：473 K
• 卷排序：49

文摘

Micro gas turbine-based combined cooling, heating and power (MGT-CCHP) system is in an important direction toward the development of smart buildings and district energy systems, which provides a clean, highly efficient and reliable means of producing energy for multiple use. However, the control of MGT-CCHP system is a challenge, due to its behavior such as large thermal inertia, and strong coupling among multi-variables. For this reasons, this paper develops a data-driven predictive controller for the MGT-CCHP system to improve its operating performance. The technique of subspace identification is utilized to construct the predictor directly from the input-output data, which can be used to estimate the future behavior of the system. The predictive controller is then designed to regulate the multi-variable MGT-CCHP system under the input-constraints. The effectiveness of the proposed control approach is demonstrated through simulation results on an 80kw MGT-CCHP simulator.