Data-driven modeling of a CO2 refrigeration system
Peer reviewed, Journal article
Accepted version
![Thumbnail](/ntnu-xmlui/bitstream/handle/11250/2650124/ACC19_preprint.pdf.jpg?sequence=5&isAllowed=y)
Åpne
Permanent lenke
https://hdl.handle.net/11250/2650124Utgivelsesdato
2019Metadata
Vis full innførselSamlinger
Originalversjon
American Control Conference (ACC). 2019, 2019-July 5385-5390. 10.23919/ACC.2019.8814871Sammendrag
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.