Case Study: Utilising of Deep Learning Models for Fault Detection and Diagnosis of Photovoltaic Modules to Improve Solar Energy Constructions’ O&M Activities Quality
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10257187" target="_blank" >RIV/61989100:27240/23:10257187 - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85197537225&origin=recordpage" target="_blank" >https://www.scopus.com/record/display.uri?eid=2-s2.0-85197537225&origin=recordpage</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-99-4792-8_5" target="_blank" >10.1007/978-981-99-4792-8_5</a>
Alternative languages
Result language
angličtina
Original language name
Case Study: Utilising of Deep Learning Models for Fault Detection and Diagnosis of Photovoltaic Modules to Improve Solar Energy Constructions’ O&M Activities Quality
Original language description
Renewable energy sources have long been considered to be the sole alternatives to fossil fuels. Consequently, the usage of PV systems has experienced exponential growth. This growth, however, places gargantuan pressure on the solar energy industry’s manufacturing sector and subsequently begets issues associated with the quality of PV systems, especially the PV module, which is the systems’ most crucial component. Currently, fault detection and diagnosis are challenging due to many factors including but not limited to requirements of sophisticated measurement instruments and experts. Recent advances in deep learning have proven its feasibility in image classification and object detection. Thus, deep learning can be extended to visual fault detection using data generated by electroluminescence imaging instruments. Here, the authors propose an in-depth approach to exploratory data analysis of electroluminescence data, as well as several techniques based on both supervised and unsupervised learning to detect and diagnose visual faults and defects presented in a module.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Information Systems Research in Vietnam, Volume 2: A Shared Vision and New Frontiers
ISBN
978-981-9947-91-1
Number of pages of the result
15
Pages from-to
53-67
Number of pages of the book
67
Publisher name
Springer
Place of publication
Singapur
UT code for WoS chapter
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