Soiling type classification and prediction of power loss of a PV panel using CNN
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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Brac University
2021
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| Truy cập trực tuyến: | http://hdl.handle.net/10361/14426 |
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10361-14426 |
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10361-144262021-05-24T21:01:19Z Soiling type classification and prediction of power loss of a PV panel using CNN Ahmed, Alvi Nur, Tahseen Md Zahid, Omar Tanzim Ali, Elma Minaz Bhuian, Mohammed Belal Hossain Department of Electrical and Electronic Engineering, Brac University Soiling Soiling type classification PV Panel CNN 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 37-39). With the rapid advancement of technology, greener and more efficient means for energy sources are always sought after. Harvesting solar energy is an effective way to generate electricity. Unfortunately, PV panel surface soiling is a major disruption in energy harvesting since it massively lowers the ability of the solar panel to be exposed to sunlight. Given how dire the air pollution situation is in Bangladesh, this is undoubtedly one of the major problems which have to be addressed when it comes to solar panels setup. When thousands of solar panels are setup in a remote location in which sunlight is abundantly available, the PV panel site has to be monitored to check if there are any issues, one of the issues being soiling. Manually checking thousands of PV panel images for soiling is laborious and time-intensive. We intend to automate that process using a lightweight deep learning model that can be incorporated into any system with fairly average computational power. More specifically, our deep learning model can determine if a particular PV panel is clean or soiled and classify the type of soiling. It can also make an approximate power loss prediction through image classification. This process will massively optimize the process of monitoring and negate the need for manually checking all the PV panels for soiling. In this paper, we propose the aforementioned deep learning model and discuss in detail how it has been developed from scratch and how feasible it is Alvi Ahmed Tahseen Md. Nur Omar Tanzim Zahid Elma Minaz Ali B. Electrical and Electronic Engineering 2021-05-24T07:24:18Z 2021-05-24T07:24:18Z 2020 2020-10 Thesis ID: 14321028 ID: 19321049 ID: 16121068 ID: 16121096 http://hdl.handle.net/10361/14426 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. 39 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Soiling Soiling type classification PV Panel CNN |
| spellingShingle |
Soiling Soiling type classification PV Panel CNN Ahmed, Alvi Nur, Tahseen Md Zahid, Omar Tanzim Ali, Elma Minaz Soiling type classification and prediction of power loss of a PV panel using CNN |
| 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 |
Bhuian, Mohammed Belal Hossain |
| author_facet |
Bhuian, Mohammed Belal Hossain Ahmed, Alvi Nur, Tahseen Md Zahid, Omar Tanzim Ali, Elma Minaz |
| format |
Thesis |
| author |
Ahmed, Alvi Nur, Tahseen Md Zahid, Omar Tanzim Ali, Elma Minaz |
| author_sort |
Ahmed, Alvi |
| title |
Soiling type classification and prediction of power loss of a PV panel using CNN |
| title_short |
Soiling type classification and prediction of power loss of a PV panel using CNN |
| title_full |
Soiling type classification and prediction of power loss of a PV panel using CNN |
| title_fullStr |
Soiling type classification and prediction of power loss of a PV panel using CNN |
| title_full_unstemmed |
Soiling type classification and prediction of power loss of a PV panel using CNN |
| title_sort |
soiling type classification and prediction of power loss of a pv panel using cnn |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14426 |
| work_keys_str_mv |
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| _version_ |
1814307640190173184 |