Faults Classification in Grid-Connected Photovoltaic Systems

Khadija Attouri; Mansour Hajji; Majdi Mansouri; Hazem Nounou; Abdelmalek Kouadri; Kais Bouzrara

doi:10.1109/SSD52085.2021.9429312

Faults Classification in Grid-Connected Photovoltaic Systems

Khadija Attouri, Mansour Hajji, Majdi Mansouri, Hazem Nounou, Abdelmalek Kouadri, Kais Bouzrara

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

2 Citations (Scopus)

Abstract

Fault detection and diagnosis (FDD) for Grid-Connected Photovoltaic (GCPV) systems have been received an important measure for improving the operation of these systems. Therefore, in this paper, an enhanced FDD approach, so-called principal component analysis (PCA)-based on a Kullback-Leibler Divergence (KLD), aims to provide the reliability and safety of the overall GCPV system is proposed. The developed approach merges the benefits of PCA model and KLD metric. Firstly, the GCPV features are extracted using PCA model. Secondly, the extracted features are fed to KLD metric for classification purposes. The obtained results confirm the high accuracy of the developed technique. The proposed approach showed superior effectiveness and robustness in process fault diagnosis.

Original language	English
Title of host publication	18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1431-1437
Number of pages	7
ISBN (Electronic)	9781665414937
DOIs	https://doi.org/10.1109/SSD52085.2021.9429312
Publication status	Published - 22 Mar 2021
Externally published	Yes
Event	18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021 - Monastir, Tunisia Duration: 22 Mar 2021 → 25 Mar 2021

Publication series

Name	18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021

Conference

Conference	18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021
Country/Territory	Tunisia
City	Monastir
Period	22/03/21 → 25/03/21

Keywords

Fault detection and diagnosis (FDD)
Grid-Connected photovoltaic systems (GCPV)
Kullback-Leibler Divergence (KLD)
Principal Component Analysis (PCA)

Access to Document

10.1109/SSD52085.2021.9429312

Cite this

Attouri, K., Hajji, M., Mansouri, M., Nounou, H., Kouadri, A., & Bouzrara, K. (2021). Faults Classification in Grid-Connected Photovoltaic Systems. In 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021 (pp. 1431-1437). Article 9429312 (18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSD52085.2021.9429312

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title = "Faults Classification in Grid-Connected Photovoltaic Systems",

abstract = "Fault detection and diagnosis (FDD) for Grid-Connected Photovoltaic (GCPV) systems have been received an important measure for improving the operation of these systems. Therefore, in this paper, an enhanced FDD approach, so-called principal component analysis (PCA)-based on a Kullback-Leibler Divergence (KLD), aims to provide the reliability and safety of the overall GCPV system is proposed. The developed approach merges the benefits of PCA model and KLD metric. Firstly, the GCPV features are extracted using PCA model. Secondly, the extracted features are fed to KLD metric for classification purposes. The obtained results confirm the high accuracy of the developed technique. The proposed approach showed superior effectiveness and robustness in process fault diagnosis.",

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author = "Khadija Attouri and Mansour Hajji and Majdi Mansouri and Hazem Nounou and Abdelmalek Kouadri and Kais Bouzrara",

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doi = "10.1109/SSD52085.2021.9429312",

language = "English",

series = "18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021",

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Attouri, K, Hajji, M, Mansouri, M, Nounou, H, Kouadri, A & Bouzrara, K 2021, Faults Classification in Grid-Connected Photovoltaic Systems. in 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021., 9429312, 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1431-1437, 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021, Monastir, Tunisia, 22/03/21. https://doi.org/10.1109/SSD52085.2021.9429312

Faults Classification in Grid-Connected Photovoltaic Systems. / Attouri, Khadija; Hajji, Mansour; Mansouri, Majdi et al.
18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1431-1437 9429312 (18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021).

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

TY - GEN

T1 - Faults Classification in Grid-Connected Photovoltaic Systems

AU - Attouri, Khadija

AU - Hajji, Mansour

AU - Mansouri, Majdi

AU - Nounou, Hazem

AU - Kouadri, Abdelmalek

AU - Bouzrara, Kais

PY - 2021/3/22

Y1 - 2021/3/22

N2 - Fault detection and diagnosis (FDD) for Grid-Connected Photovoltaic (GCPV) systems have been received an important measure for improving the operation of these systems. Therefore, in this paper, an enhanced FDD approach, so-called principal component analysis (PCA)-based on a Kullback-Leibler Divergence (KLD), aims to provide the reliability and safety of the overall GCPV system is proposed. The developed approach merges the benefits of PCA model and KLD metric. Firstly, the GCPV features are extracted using PCA model. Secondly, the extracted features are fed to KLD metric for classification purposes. The obtained results confirm the high accuracy of the developed technique. The proposed approach showed superior effectiveness and robustness in process fault diagnosis.

AB - Fault detection and diagnosis (FDD) for Grid-Connected Photovoltaic (GCPV) systems have been received an important measure for improving the operation of these systems. Therefore, in this paper, an enhanced FDD approach, so-called principal component analysis (PCA)-based on a Kullback-Leibler Divergence (KLD), aims to provide the reliability and safety of the overall GCPV system is proposed. The developed approach merges the benefits of PCA model and KLD metric. Firstly, the GCPV features are extracted using PCA model. Secondly, the extracted features are fed to KLD metric for classification purposes. The obtained results confirm the high accuracy of the developed technique. The proposed approach showed superior effectiveness and robustness in process fault diagnosis.

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KW - Grid-Connected photovoltaic systems (GCPV)

KW - Kullback-Leibler Divergence (KLD)

KW - Principal Component Analysis (PCA)

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M3 - Conference contribution

AN - SCOPUS:85107519134

T3 - 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021

SP - 1431

EP - 1437

BT - 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021

Y2 - 22 March 2021 through 25 March 2021

ER -

Attouri K, Hajji M, Mansouri M, Nounou H, Kouadri A, Bouzrara K. Faults Classification in Grid-Connected Photovoltaic Systems. In 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1431-1437. 9429312. (18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021). doi: 10.1109/SSD52085.2021.9429312

Faults Classification in Grid-Connected Photovoltaic Systems

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Keywords

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