Fault Diagnosis Based on Machine Learning for the High Frequency Link of a Grid-Tied Photovoltaic Converter for a Wide Range of Irradiance Conditions

The objective of this work is to select a Machine Learning Technique (MLT) to develop a fault diagnosis scheme for the power switching devices of the High Frequency link (HF link) in a grid-tied Photovoltaic (PV) system, without increasing the total number of sensors, and being capable to operate on...

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Autores principales:	Yuniel Leon-Ruiz, Mario Gonzalez-Garcia, Ricardo Alvarez-Salas, Juan Cuevas-Tello, Victor Cardenas
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Fault diagnosis high frequency link machine learning photovoltaic systems Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/92bcd048bbb1438eb82ee41b2c6b3d6a
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spelling	oai:doaj.org-article:92bcd048bbb1438eb82ee41b2c6b3d6a2021-11-17T00:01:01ZFault Diagnosis Based on Machine Learning for the High Frequency Link of a Grid-Tied Photovoltaic Converter for a Wide Range of Irradiance Conditions2169-353610.1109/ACCESS.2021.3126706https://doaj.org/article/92bcd048bbb1438eb82ee41b2c6b3d6a2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606924/https://doaj.org/toc/2169-3536The objective of this work is to select a Machine Learning Technique (MLT) to develop a fault diagnosis scheme for the power switching devices of the High Frequency link (HF link) in a grid-tied Photovoltaic (PV) system, without increasing the total number of sensors, and being capable to operate online. Artificial Neural Network (ANN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Naive Bayes (NB) algorithms are considered to solve the problem of fault classification. These four MLTs are compared using the specificity and sensitivity indexes. The inputs of the models are obtained from the mean value of the signals given by the Discrete Wavelet Transform (DWT) of the dc link voltage and the power extracted from the PV panels. Support vector machine algorithm is chosen as the most suitable classifier to diagnose single and simultaneous open circuit faults with lower computational effort. Simulation and real-time hardware-based experimental tests demonstrate that the MLTs are suitable and reliable to diagnose open circuit faults in a wide range of irradiance levels, ranging from 200 W/m<sup>2</sup> to 1000 W/m<sup>2</sup>, even under 6 % and 12 % measurement errors, without increasing the overall system cost.Yuniel Leon-RuizMario Gonzalez-GarciaRicardo Alvarez-SalasJuan Cuevas-TelloVictor CardenasIEEEarticleFault diagnosishigh frequency linkmachine learningphotovoltaic systemsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151209-151220 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Fault diagnosis high frequency link machine learning photovoltaic systems Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Fault diagnosis high frequency link machine learning photovoltaic systems Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yuniel Leon-Ruiz Mario Gonzalez-Garcia Ricardo Alvarez-Salas Juan Cuevas-Tello Victor Cardenas Fault Diagnosis Based on Machine Learning for the High Frequency Link of a Grid-Tied Photovoltaic Converter for a Wide Range of Irradiance Conditions
description	The objective of this work is to select a Machine Learning Technique (MLT) to develop a fault diagnosis scheme for the power switching devices of the High Frequency link (HF link) in a grid-tied Photovoltaic (PV) system, without increasing the total number of sensors, and being capable to operate online. Artificial Neural Network (ANN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Naive Bayes (NB) algorithms are considered to solve the problem of fault classification. These four MLTs are compared using the specificity and sensitivity indexes. The inputs of the models are obtained from the mean value of the signals given by the Discrete Wavelet Transform (DWT) of the dc link voltage and the power extracted from the PV panels. Support vector machine algorithm is chosen as the most suitable classifier to diagnose single and simultaneous open circuit faults with lower computational effort. Simulation and real-time hardware-based experimental tests demonstrate that the MLTs are suitable and reliable to diagnose open circuit faults in a wide range of irradiance levels, ranging from 200 W/m<sup>2</sup> to 1000 W/m<sup>2</sup>, even under 6 % and 12 % measurement errors, without increasing the overall system cost.
format	article
author	Yuniel Leon-Ruiz Mario Gonzalez-Garcia Ricardo Alvarez-Salas Juan Cuevas-Tello Victor Cardenas
author_facet	Yuniel Leon-Ruiz Mario Gonzalez-Garcia Ricardo Alvarez-Salas Juan Cuevas-Tello Victor Cardenas
author_sort	Yuniel Leon-Ruiz
title	Fault Diagnosis Based on Machine Learning for the High Frequency Link of a Grid-Tied Photovoltaic Converter for a Wide Range of Irradiance Conditions
title_short	Fault Diagnosis Based on Machine Learning for the High Frequency Link of a Grid-Tied Photovoltaic Converter for a Wide Range of Irradiance Conditions
title_full	Fault Diagnosis Based on Machine Learning for the High Frequency Link of a Grid-Tied Photovoltaic Converter for a Wide Range of Irradiance Conditions
title_fullStr	Fault Diagnosis Based on Machine Learning for the High Frequency Link of a Grid-Tied Photovoltaic Converter for a Wide Range of Irradiance Conditions
title_full_unstemmed	Fault Diagnosis Based on Machine Learning for the High Frequency Link of a Grid-Tied Photovoltaic Converter for a Wide Range of Irradiance Conditions
title_sort	fault diagnosis based on machine learning for the high frequency link of a grid-tied photovoltaic converter for a wide range of irradiance conditions
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/92bcd048bbb1438eb82ee41b2c6b3d6a
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Fault Diagnosis Based on Machine Learning for the High Frequency Link of a Grid-Tied Photovoltaic Converter for a Wide Range of Irradiance Conditions

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