An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems

The main objective of this article is to develop an enhanced ensemble learning (EL) based intelligent fault detection and diagnosis (FDD) paradigms that aim to ensure the high-performance operation of Grid-Connected Photovoltaic (PV) systems. The developed EL based techniques consist in combining mu...

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Autores principales:	Khaled Dhibi, Majdi Mansouri, Kais Bouzrara, Hazem Nounou, Mohamed Nounou
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Machine learning ensemble learning kernel principal component analysis (KPCA) fault detection fault diagnosis fault classification Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/4f18df02a3ac41c6b95ee779271133d6
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id	oai:doaj.org-article:4f18df02a3ac41c6b95ee779271133d6
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spelling	oai:doaj.org-article:4f18df02a3ac41c6b95ee779271133d62021-11-26T00:00:57ZAn Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems2169-353610.1109/ACCESS.2021.3128749https://doaj.org/article/4f18df02a3ac41c6b95ee779271133d62021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9617623/https://doaj.org/toc/2169-3536The main objective of this article is to develop an enhanced ensemble learning (EL) based intelligent fault detection and diagnosis (FDD) paradigms that aim to ensure the high-performance operation of Grid-Connected Photovoltaic (PV) systems. The developed EL based techniques consist in combining multiple learning models instead of using a single learning model. To do that, three EL-based FDD techniques are proposed. First, an EL technique that merges the benefits of Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT) is presented. The developed method contributes to the reduction of the overall diagnosis error and has the ability to combine various models. However, classical EL models ignore the time-dependence of PV measurements. In addition, the PV system data are frequently time-correlated. Therefore, kernel PCA (KPCA)-based EL and reduced KPCA (RKPCA)-based EL techniques are developed to take into consideration the dynamic and multivariate natures of the PV measurements. The two proposed KPCA -based EL and RKPCA-based EL techniques are addressed so that the features extraction and selection phases are performed using the KPCA and RKPCA models and the sensitive and significant characteristics are transmitted to the EL model for classification purposes. The presented results prove that the proposed EL based methods offer enhanced diagnosis performances when applied to PV systems.Khaled DhibiMajdi MansouriKais BouzraraHazem NounouMohamed NounouIEEEarticleMachine learningensemble learningkernel principal component analysis (KPCA)fault detectionfault diagnosisfault classificationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155622-155633 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning ensemble learning kernel principal component analysis (KPCA) fault detection fault diagnosis fault classification Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Machine learning ensemble learning kernel principal component analysis (KPCA) fault detection fault diagnosis fault classification Electrical engineering. Electronics. Nuclear engineering TK1-9971 Khaled Dhibi Majdi Mansouri Kais Bouzrara Hazem Nounou Mohamed Nounou An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems
description	The main objective of this article is to develop an enhanced ensemble learning (EL) based intelligent fault detection and diagnosis (FDD) paradigms that aim to ensure the high-performance operation of Grid-Connected Photovoltaic (PV) systems. The developed EL based techniques consist in combining multiple learning models instead of using a single learning model. To do that, three EL-based FDD techniques are proposed. First, an EL technique that merges the benefits of Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT) is presented. The developed method contributes to the reduction of the overall diagnosis error and has the ability to combine various models. However, classical EL models ignore the time-dependence of PV measurements. In addition, the PV system data are frequently time-correlated. Therefore, kernel PCA (KPCA)-based EL and reduced KPCA (RKPCA)-based EL techniques are developed to take into consideration the dynamic and multivariate natures of the PV measurements. The two proposed KPCA -based EL and RKPCA-based EL techniques are addressed so that the features extraction and selection phases are performed using the KPCA and RKPCA models and the sensitive and significant characteristics are transmitted to the EL model for classification purposes. The presented results prove that the proposed EL based methods offer enhanced diagnosis performances when applied to PV systems.
format	article
author	Khaled Dhibi Majdi Mansouri Kais Bouzrara Hazem Nounou Mohamed Nounou
author_facet	Khaled Dhibi Majdi Mansouri Kais Bouzrara Hazem Nounou Mohamed Nounou
author_sort	Khaled Dhibi
title	An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems
title_short	An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems
title_full	An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems
title_fullStr	An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems
title_full_unstemmed	An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems
title_sort	enhanced ensemble learning-based fault detection and diagnosis for grid-connected pv systems
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/4f18df02a3ac41c6b95ee779271133d6
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An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems

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