Resumé
This paper describes a data-driven method for system identification of a CO2 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.
| Originalsprog | Engelsk |
|---|---|
| Titel | 2019 American Control Conference (ACC) |
| Antal sider | 6 |
| Forlag | IEEE |
| Publikationsdato | 29 aug. 2019 |
| Sider | 5385-5390 |
| ISBN (Trykt) | 978-1-5386-7901-2 |
| ISBN (Elektronisk) | 978-1-5386-7926-5 |
| Status | Udgivet - 29 aug. 2019 |
| Begivenhed | 2019 American Control Conference (ACC) - Philadelphia, USA Varighed: 10 jul. 2019 → 12 jul. 2019 |
Konference
| Konference | 2019 American Control Conference (ACC) |
|---|---|
| Land | USA |
| By | Philadelphia |
| Periode | 10/07/2019 → 12/07/2019 |
| Navn | American Control Conference |
|---|---|
| ISSN | 0743-1619 |
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Data-Driven Modeling of a CO2 Refrigeration System. / Andreasen, Glenn; Stoustrup, Jakob; Izadi-Zamanabadi, Roozbeh; Á. Pardiñas, Ángel ; Hafner, Armin.
2019 American Control Conference (ACC). IEEE, 2019. s. 5385-5390 (American Control Conference).Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review
TY - GEN
T1 - Data-Driven Modeling of a CO2 Refrigeration System
AU - Andreasen, Glenn
AU - Stoustrup, Jakob
AU - Izadi-Zamanabadi, Roozbeh
AU - Á. Pardiñas, Ángel
AU - Hafner, Armin
PY - 2019/8/29
Y1 - 2019/8/29
N2 - This paper describes a data-driven method for system identification of a CO2 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.
AB - This paper describes a data-driven method for system identification of a CO2 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.
UR - https://ieeexplore.ieee.org/document/8814871
M3 - Article in proceeding
SN - 978-1-5386-7901-2
T3 - American Control Conference
SP - 5385
EP - 5390
BT - 2019 American Control Conference (ACC)
PB - IEEE
ER -