Publication
Fault detection and diagnosis technique for a SRM drive based on a multilevel converter using a machine learning approach
| dc.contributor.author | Amaral, Tito G. | |
| dc.contributor.author | Pires, Vítor | |
| dc.contributor.author | Foito, Daniel José Medronho | |
| dc.contributor.author | Pires, A. J. | |
| dc.contributor.author | Martins, J. F. | |
| dc.date.accessioned | 2023-09-05T10:55:32Z | |
| dc.date.available | 2023-09-05T10:55:32Z | |
| dc.date.issued | 2023-08 | |
| dc.description | Trabalho apresentado em 12th International Conference on Renewable Energy Research and Applications (ICRERA 2023), 29 augusto-1 setembro 2023, Oshawa, Canada | pt_PT |
| dc.description.abstract | SRM drives based on multilevel converters is now a solution well accepted due to their interesting features like extended voltage range and capability to fault tolerance. However, one aspect that is fundamental to ensure fault tolerance or preventive maintenance is the fault detection and diagnosis of failures in power semiconductors. In this way, in this paper it is presented a new diagnostic method for the failure of those semiconductors in asymmetric neutral point clamped converters. The proposed method will be based on the development of specific patterns that are associated to each semiconductor and fault type. The procedures presented here are based on the image identification of the currents patterns in the multilevel converter that allow the identification of distinct fault type. The pattern recognition system uses visual-based efficient invariants features for continuous monitoring of multilevel converter The proposed method will be verified through several tests in which were used a simulation tool and an experimental prototype. | pt_PT |
| dc.description.version | N/A | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.26/46397 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.subject | Fault Diagnosis | pt_PT |
| dc.subject | Detection, pattern recognition | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | SRM | pt_PT |
| dc.subject | Multilevel converters | pt_PT |
| dc.title | Fault detection and diagnosis technique for a SRM drive based on a multilevel converter using a machine learning approach | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| person.familyName | Amaral | |
| person.familyName | Medronho Foito | |
| person.familyName | Pinheiro Marques Pires | |
| person.givenName | Tito Gerardo Batoreo | |
| person.givenName | Daniel José | |
| person.givenName | Armando José | |
| person.identifier.ciencia-id | C31E-631E-2AD8 | |
| person.identifier.ciencia-id | B81F-6757-DFBB | |
| person.identifier.ciencia-id | DF15-7E08-AAB6 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
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