Data-driven modeling of a CO2 refrigeration system
Peer reviewed, Journal article
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Original versionAmerican Control Conference (ACC). 2019, 2019-July 5385-5390. 10.23919/ACC.2019.8814871
This paper describes a data-driven method for system identification of a CO 2 refrigeration system. Traditionally, the interaction between the measured variables is not utilized as they are highly dependent on the refrigeration system. In this work a data-driven method, namely subspace identification, is investigated for deriving a control-oriented model such that the dynamic interaction in the refrigeration systems can be utilized for e.g. fault detection and diagnosis. The subspace identification is applied on laboratory data obtained from a test setup located at NTNU in Trondheim, Norway. The obtained results offer promising perspectives for performance improvement in fault detection and diagnosis methods as well as control strategies.