A one-class classifier based on a hybrid topology to detect faults in power cells
![Thumbnail](/dspace/bitstream/handle/2183/32285/Jove_Esteban_2022_One-class-classifier-hybrid-topology-detect-faults-power-cells.pdf.jpg?sequence=5&isAllowed=y)
View/ Open
Use this link to cite
http://hdl.handle.net/2183/32285
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)
Collections
- GI-CTC - Artigos [81]
Metadata
Show full item recordTitle
A one-class classifier based on a hybrid topology to detect faults in power cellsAuthor(s)
Date
2022-08-01Citation
Esteban Jove, José-Luis Casteleiro-Roca, Héctor Quintián, Francisco Zayas-Gato, Gianni Vercelli, José Luis Calvo-Rolle, A One-class Classifier Based on a Hybrid Topology to Detect Faults in Power Cells, Logic Journal of the IGPL, Volume 30, Issue 4, August 2022, Pages 679–694, https://doi.org/10.1093/jigpal/jzab011
Abstract
[Abstract]: The use of batteries became essential in our daily life in electronic devices, electric vehicles and energy storage systems in general terms. As they play a key role in many devices, their design and implementation must follow a thorough test process to check their features at different operating points. In this circumstance, the appearance of any kind of deviation from the expected operation must be detected. This research deals with real data registered during the testing phase of a lithium iron phosphate—LiFePO4—battery. The process is divided into four different working points, alternating charging, discharging and resting periods. This work proposes a hybrid classifier, based on one-class techniques, whose aim is to detect anomalous situations during the battery test. The faults are created by modifying the measured cell temperature a slight ratio from their real value. A detailed analysis of each technique performance is presented. The average performance of the chosen classifier presents successful results
Keywords
Fault detection
One-lass
Gauss
MST
SVDD
PCA
PDE
One-lass
Gauss
MST
SVDD
PCA
PDE
Editor version
Rights
Attribution 4.0 International (CC BY 4.0)
ISSN
1368-9894
1367-0751
1367-0751