Autonomous fault diagnosis of commercially available PV modules using high-end deep learning frameworks

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021.

Bibliographic Details
Main Authors:	Tasawar, Ihtyaz Kader, Tanzeem, Abyaz Kader, Ahmed, Tahmid, Zarin, Shah Faiza
Other Authors:	Rahman, Md. Mosaddequr
Format:	Thesis
Language:	English
Published:	Brac University 2021
Subjects:	Deep Neural Network Convolutional Neural Network Infrared Image Processing Photovoltaic Cell Fault Diagnosis Hotspot Detection Deep Learning
Online Access:	http://hdl.handle.net/10361/15153

id	10361-15153
record_format	dspace
spelling	10361-151532021-10-06T21:01:19Z Autonomous fault diagnosis of commercially available PV modules using high-end deep learning frameworks Tasawar, Ihtyaz Kader Tanzeem, Abyaz Kader Ahmed, Tahmid Zarin, Shah Faiza Rahman, Md. Mosaddequr Department of Electrical and Electronic Engineering, Brac University Deep Neural Network Convolutional Neural Network Infrared Image Processing Photovoltaic Cell Fault Diagnosis Hotspot Detection Deep Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 76-82). Conventional methods of fault diagnosis for PV Systems are quite challenging and inefficient, particularly with regards to large-scale PV arrays. Early and effective diagnosis of system faults is also imperative in order to minimize cost and sustainable damage. Hence, over the recent years, numerous effective and efficient monitoring and diagnostic techniques to detect faults in PV systems have been studied and propositioned. As such, autonomous fault diagnosis and classification of PV systems has taken the PV domain by storm and has spectacularly developed in eminence; attaining substantial significance in the domain of deep learning. Over the last few years, various deep learning frameworks have been studied and proposed in the detection & classification of faults in PV modules with the aid of thermal images. Some of the most prominent deep learning frameworks constitutes of ANN & CNN. This study involves utilization of Convolutional Neural Networks (CNN), namely, VGG-16/VGG-19 and EfficientNet, in order to assess their performance and reliability in diagnosing module defects through significant hotpots within PV modules by employing pre-processed thermal images. Ihtyaz Kader Tasawar Abyaz Kader Tanzeem Tahmid Ahmed Shah Faiza Zarin B. Electrical and Electronic Engineering 2021-10-06T06:55:06Z 2021-10-06T06:55:06Z 2021 2021-06 Thesis ID 17321038 ID 17321039 ID 17121095 ID 17121037 http://hdl.handle.net/10361/15153 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 82 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Deep Neural Network Convolutional Neural Network Infrared Image Processing Photovoltaic Cell Fault Diagnosis Hotspot Detection Deep Learning
spellingShingle	Deep Neural Network Convolutional Neural Network Infrared Image Processing Photovoltaic Cell Fault Diagnosis Hotspot Detection Deep Learning Tasawar, Ihtyaz Kader Tanzeem, Abyaz Kader Ahmed, Tahmid Zarin, Shah Faiza Autonomous fault diagnosis of commercially available PV modules using high-end deep learning frameworks
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021.
author2	Rahman, Md. Mosaddequr
author_facet	Rahman, Md. Mosaddequr Tasawar, Ihtyaz Kader Tanzeem, Abyaz Kader Ahmed, Tahmid Zarin, Shah Faiza
format	Thesis
author	Tasawar, Ihtyaz Kader Tanzeem, Abyaz Kader Ahmed, Tahmid Zarin, Shah Faiza
author_sort	Tasawar, Ihtyaz Kader
title	Autonomous fault diagnosis of commercially available PV modules using high-end deep learning frameworks
title_short	Autonomous fault diagnosis of commercially available PV modules using high-end deep learning frameworks
title_full	Autonomous fault diagnosis of commercially available PV modules using high-end deep learning frameworks
title_fullStr	Autonomous fault diagnosis of commercially available PV modules using high-end deep learning frameworks
title_full_unstemmed	Autonomous fault diagnosis of commercially available PV modules using high-end deep learning frameworks
title_sort	autonomous fault diagnosis of commercially available pv modules using high-end deep learning frameworks
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15153
work_keys_str_mv	AT tasawarihtyazkader autonomousfaultdiagnosisofcommerciallyavailablepvmodulesusinghighenddeeplearningframeworks AT tanzeemabyazkader autonomousfaultdiagnosisofcommerciallyavailablepvmodulesusinghighenddeeplearningframeworks AT ahmedtahmid autonomousfaultdiagnosisofcommerciallyavailablepvmodulesusinghighenddeeplearningframeworks AT zarinshahfaiza autonomousfaultdiagnosisofcommerciallyavailablepvmodulesusinghighenddeeplearningframeworks
_version_	1814308817016455168

Autonomous fault diagnosis of commercially available PV modules using high-end deep learning frameworks

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