Paper Abstract and Keywords |
Presentation |
2017-01-20 14:40
Defect Causal Analysis in Solar Panel using Thermal Image : A Deep Learning Approach Seungho Lee, Makoto Suzuki, Hiroyuki Morikawa (UTokyo) ASN2016-87 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Deteriorated solar panels cause not only decreasing of power generation but also significantly safety concerns such as concentrated heat generation in a small area. Therefore, the deterioration diagnosis and the risk management are indispensable for solar power generation systems. However, conventional maintenance methods focusing on the power generation characteristics costs a lot of time and money since it is necessary to connect the measuring device to each solar module. Conventional maintenance methods using thermal images are mainly based on abnormality detection focusing on specifying the high temperature area, and specific diagnosis in abnormal solar cells has not yet been provided. In this paper, we design the thermal image classifier using deep learning for the state diagnosis of the bypass diode and the power generation circuit which are the main abnormal part of solar module. The validation based on 1400 thermal images of six solar modules collected over six months, shown to classification performance of f1-score 0.97. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Solar panel / Fault diagnosis / Thermal image / Convolutional neural network / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 116, no. 407, ASN2016-87, pp. 95-100, Jan. 2017. |
Paper # |
ASN2016-87 |
Date of Issue |
2017-01-12 (ASN) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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ASN2016-87 |
Conference Information |
Committee |
ASN MoNA MICT |
Conference Date |
2017-01-19 - 2017-01-20 |
Place (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
ASN |
Conference Code |
2017-01-ASN-MoNA-MICT |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Defect Causal Analysis in Solar Panel using Thermal Image : A Deep Learning Approach |
Sub Title (in English) |
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Keyword(1) |
Solar panel |
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Fault diagnosis |
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Thermal image |
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Convolutional neural network |
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1st Author's Name |
Seungho Lee |
1st Author's Affiliation |
Research Center for Advanced Science and Technology, The University of Tokyo (UTokyo) |
2nd Author's Name |
Makoto Suzuki |
2nd Author's Affiliation |
Research Center for Advanced Science and Technology, The University of Tokyo (UTokyo) |
3rd Author's Name |
Hiroyuki Morikawa |
3rd Author's Affiliation |
Research Center for Advanced Science and Technology, The University of Tokyo (UTokyo) |
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Speaker |
Author-1 |
Date Time |
2017-01-20 14:40:00 |
Presentation Time |
25 minutes |
Registration for |
ASN |
Paper # |
ASN2016-87 |
Volume (vol) |
vol.116 |
Number (no) |
no.407 |
Page |
pp.95-100 |
#Pages |
6 |
Date of Issue |
2017-01-12 (ASN) |
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