Infrared thermography based defect analysis of photovoltaic modules using machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2020.
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| フォーマット: | 学位論文 |
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Brac University
2021
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| オンライン･アクセス: | http://hdl.handle.net/10361/14367 |
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10361-143672021-03-21T21:01:18Z Infrared thermography based defect analysis of photovoltaic modules using machine learning Mobin, Ovib Hassan Tajwar, Tahmid Khan, Fariha Reza Hossain, Shara Fatema Rahman, Md. Mosaddequr Department of Electrical and Electronic Engineering, Brac University Infrared thermography YOLO Photovoltaic Hotspot Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 71-74). Solar photovoltaic (PV) has gained much attention throughout the world for clean energy production. Faults in the PV modules cause the reduction of the amount of the electricity gain from the PV systems. Detecting faults of PV modules could help to take necessary measures. In this study, Infrared thermography (IRT) is used in order to take images of PV modules which may indicate the hotspot. Later, these images are converted into datasets for a classifier to detect the hotspot of PV modules. Besides, I-V characteristics of the PV modules are also analyzed to find out the relation between hotspot & defected area. The further part of this study has been conducted by using a machine learning tool called ‘YOLO: You Only Look once’. After training & testing the learner with the datasets, the outputs are validated with the IRT images of PV modules. The major gain of this study is to apply a modified real time object detection tool to understand and detect the defect of the PV module. The algorithm is capable of detecting the hotspot using the weightage file of the training phase. Result shows that with more diversified datasets the accuracy of detecting hotspot increases. Ovib Hassan Mobin Tahmid Tajwar Fariha Reza Khan Shara Fatema Hossain B. Electrical and Electronic Engineering 2021-03-21T07:54:00Z 2021-03-21T07:54:00Z 2020 2020-10 Thesis ID: 16321145 ID: 16321051 ID: 16321021 ID: 16121095 http://hdl.handle.net/10361/14367 en_US 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. 76 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Infrared thermography YOLO Photovoltaic Hotspot Machine learning |
| spellingShingle |
Infrared thermography YOLO Photovoltaic Hotspot Machine learning Mobin, Ovib Hassan Tajwar, Tahmid Khan, Fariha Reza Hossain, Shara Fatema Infrared thermography based defect analysis of photovoltaic modules using machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2020. |
| author2 |
Rahman, Md. Mosaddequr |
| author_facet |
Rahman, Md. Mosaddequr Mobin, Ovib Hassan Tajwar, Tahmid Khan, Fariha Reza Hossain, Shara Fatema |
| format |
Thesis |
| author |
Mobin, Ovib Hassan Tajwar, Tahmid Khan, Fariha Reza Hossain, Shara Fatema |
| author_sort |
Mobin, Ovib Hassan |
| title |
Infrared thermography based defect analysis of photovoltaic modules using machine learning |
| title_short |
Infrared thermography based defect analysis of photovoltaic modules using machine learning |
| title_full |
Infrared thermography based defect analysis of photovoltaic modules using machine learning |
| title_fullStr |
Infrared thermography based defect analysis of photovoltaic modules using machine learning |
| title_full_unstemmed |
Infrared thermography based defect analysis of photovoltaic modules using machine learning |
| title_sort |
infrared thermography based defect analysis of photovoltaic modules using machine learning |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14367 |
| work_keys_str_mv |
AT mobinovibhassan infraredthermographybaseddefectanalysisofphotovoltaicmodulesusingmachinelearning AT tajwartahmid infraredthermographybaseddefectanalysisofphotovoltaicmodulesusingmachinelearning AT khanfarihareza infraredthermographybaseddefectanalysisofphotovoltaicmodulesusingmachinelearning AT hossainsharafatema infraredthermographybaseddefectanalysisofphotovoltaicmodulesusingmachinelearning |
| _version_ |
1814308227413704704 |