A Fault Diagnosis Scheme Based on the Normalized Indexes of the Images eccentricity for a Multilevel Converter of a Switched Reluctance Motor Drive

Tito G. Amaral; V. Fernão Pires; Daniel Foito; Armando Cordeiro; José Inácio Rocha; Miguel Chaves; A. J. Pires; J. F. Martins

doi:10.1109/ICRERA55966.2022.9922711

A Fault Diagnosis Scheme Based on the Normalized Indexes of the Images eccentricity for a Multilevel Converter of a Switched Reluctance Motor Drive

Tito G. Amaral, V. Fernão Pires, Daniel Foito, Armando Cordeiro, José Inácio Rocha, Miguel Chaves, A. J. Pires, J. F. Martins

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

This paper addresses the fault detection and diagnosis of a fault in the switches of the Switched Reluctance Machine (SRM) power electronic converter. Due to the advantages of using multilevel converters with these machines, a fault detection and diagnosis algorithm is proposed for this converter. The topology under consideration is the asymmetric Neutral Point Clamped (ANPC), and the algorithm was developed to detect open and short circuit faults. The proposed algorithm is based on an approach that discriminates eccentricity of the images formed by the converter voltages. This discrimination is realized through the development of normalized indexes based on the entropy theory. Besides the different fault type the algorithm is also able to detect the transistor under fault. The possibility to implement the proposed approach will be verified through simulation tests.

Original language	English
Title of host publication	2022 11th IEEE International Conference on Renewable Energy Research and Applications (ICRERA)
Place of Publication	New York
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	61-66
Number of pages	6
ISBN (Electronic)	978-1-6654-7140-4
ISBN (Print)	978-1-6654-7141-1
DOIs	https://doi.org/10.1109/ICRERA55966.2022.9922711
Publication status	Published - 2022
Event	11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022 - Istanbul, Turkey Duration: 18 Sept 2022 → 21 Sept 2022

Publication series

Name	International Conference on Renewable Energy Research and Applications (ICRERA)
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISSN (Print)	2572-6013
ISSN (Electronic)	2377-6897

Conference

Conference	11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022
Country/Territory	Turkey
City	Istanbul
Period	18/09/22 → 21/09/22

Keywords

asymmetric neutral point clamped
entropy
fault detection
multilevel converters
SRM

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICRERA55966.2022.9922711

Cite this

Amaral, T. G., Fernão Pires, V., Foito, D., Cordeiro, A., Rocha, J. I., Chaves, M., Pires, A. J., & Martins, J. F. (2022). A Fault Diagnosis Scheme Based on the Normalized Indexes of the Images eccentricity for a Multilevel Converter of a Switched Reluctance Motor Drive. In 2022 11th IEEE International Conference on Renewable Energy Research and Applications (ICRERA) (pp. 61-66). (International Conference on Renewable Energy Research and Applications (ICRERA)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICRERA55966.2022.9922711

Amaral, Tito G. ; Fernão Pires, V. ; Foito, Daniel et al. / A Fault Diagnosis Scheme Based on the Normalized Indexes of the Images eccentricity for a Multilevel Converter of a Switched Reluctance Motor Drive. 2022 11th IEEE International Conference on Renewable Energy Research and Applications (ICRERA). New York : Institute of Electrical and Electronics Engineers (IEEE), 2022. pp. 61-66 (International Conference on Renewable Energy Research and Applications (ICRERA)).

@inproceedings{849de5aca764469ea34437eb1fccb68b,

title = "A Fault Diagnosis Scheme Based on the Normalized Indexes of the Images eccentricity for a Multilevel Converter of a Switched Reluctance Motor Drive",

abstract = "This paper addresses the fault detection and diagnosis of a fault in the switches of the Switched Reluctance Machine (SRM) power electronic converter. Due to the advantages of using multilevel converters with these machines, a fault detection and diagnosis algorithm is proposed for this converter. The topology under consideration is the asymmetric Neutral Point Clamped (ANPC), and the algorithm was developed to detect open and short circuit faults. The proposed algorithm is based on an approach that discriminates eccentricity of the images formed by the converter voltages. This discrimination is realized through the development of normalized indexes based on the entropy theory. Besides the different fault type the algorithm is also able to detect the transistor under fault. The possibility to implement the proposed approach will be verified through simulation tests.",

keywords = "asymmetric neutral point clamped, entropy, fault detection, multilevel converters, SRM",

author = "Amaral, {Tito G.} and {Fern{\~a}o Pires}, V. and Daniel Foito and Armando Cordeiro and Rocha, {Jos{\'e} In{\'a}cio} and Miguel Chaves and Pires, {A. J.} and Martins, {J. F.}",

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Amaral, TG, Fernão Pires, V, Foito, D, Cordeiro, A, Rocha, JI, Chaves, M, Pires, AJ & Martins, JF 2022, A Fault Diagnosis Scheme Based on the Normalized Indexes of the Images eccentricity for a Multilevel Converter of a Switched Reluctance Motor Drive. in 2022 11th IEEE International Conference on Renewable Energy Research and Applications (ICRERA). International Conference on Renewable Energy Research and Applications (ICRERA), Institute of Electrical and Electronics Engineers (IEEE), New York, pp. 61-66, 11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022, Istanbul, Turkey, 18/09/22. https://doi.org/10.1109/ICRERA55966.2022.9922711

A Fault Diagnosis Scheme Based on the Normalized Indexes of the Images eccentricity for a Multilevel Converter of a Switched Reluctance Motor Drive. / Amaral, Tito G.; Fernão Pires, V.; Foito, Daniel et al.
2022 11th IEEE International Conference on Renewable Energy Research and Applications (ICRERA). New York: Institute of Electrical and Electronics Engineers (IEEE), 2022. p. 61-66 (International Conference on Renewable Energy Research and Applications (ICRERA)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Fault Diagnosis Scheme Based on the Normalized Indexes of the Images eccentricity for a Multilevel Converter of a Switched Reluctance Motor Drive

AU - Amaral, Tito G.

AU - Fernão Pires, V.

AU - Foito, Daniel

AU - Cordeiro, Armando

AU - Rocha, José Inácio

AU - Chaves, Miguel

AU - Pires, A. J.

AU - Martins, J. F.

N1 - Funding Information: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT# info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT# Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - This paper addresses the fault detection and diagnosis of a fault in the switches of the Switched Reluctance Machine (SRM) power electronic converter. Due to the advantages of using multilevel converters with these machines, a fault detection and diagnosis algorithm is proposed for this converter. The topology under consideration is the asymmetric Neutral Point Clamped (ANPC), and the algorithm was developed to detect open and short circuit faults. The proposed algorithm is based on an approach that discriminates eccentricity of the images formed by the converter voltages. This discrimination is realized through the development of normalized indexes based on the entropy theory. Besides the different fault type the algorithm is also able to detect the transistor under fault. The possibility to implement the proposed approach will be verified through simulation tests.

AB - This paper addresses the fault detection and diagnosis of a fault in the switches of the Switched Reluctance Machine (SRM) power electronic converter. Due to the advantages of using multilevel converters with these machines, a fault detection and diagnosis algorithm is proposed for this converter. The topology under consideration is the asymmetric Neutral Point Clamped (ANPC), and the algorithm was developed to detect open and short circuit faults. The proposed algorithm is based on an approach that discriminates eccentricity of the images formed by the converter voltages. This discrimination is realized through the development of normalized indexes based on the entropy theory. Besides the different fault type the algorithm is also able to detect the transistor under fault. The possibility to implement the proposed approach will be verified through simulation tests.

KW - asymmetric neutral point clamped

KW - entropy

KW - fault detection

KW - multilevel converters

KW - SRM

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DO - 10.1109/ICRERA55966.2022.9922711

M3 - Conference contribution

AN - SCOPUS:85142014389

SN - 978-1-6654-7141-1

T3 - International Conference on Renewable Energy Research and Applications (ICRERA)

SP - 61

EP - 66

BT - 2022 11th IEEE International Conference on Renewable Energy Research and Applications (ICRERA)

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - New York

T2 - 11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022

Y2 - 18 September 2022 through 21 September 2022

ER -

Amaral TG, Fernão Pires V, Foito D, Cordeiro A, Rocha JI, Chaves M et al. A Fault Diagnosis Scheme Based on the Normalized Indexes of the Images eccentricity for a Multilevel Converter of a Switched Reluctance Motor Drive. In 2022 11th IEEE International Conference on Renewable Energy Research and Applications (ICRERA). New York: Institute of Electrical and Electronics Engineers (IEEE). 2022. p. 61-66. (International Conference on Renewable Energy Research and Applications (ICRERA)). doi: 10.1109/ICRERA55966.2022.9922711

A Fault Diagnosis Scheme Based on the Normalized Indexes of the Images eccentricity for a Multilevel Converter of a Switched Reluctance Motor Drive

Abstract

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Keywords

UN SDGs

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