Publication:
Solar cell fault monitoring using image processing approach

dc.contributor.authorFiona Sharon Hilaryen_US
dc.date.accessioned2023-05-03T16:15:58Z
dc.date.available2023-05-03T16:15:58Z
dc.date.issued2020-02
dc.descriptionFYP 2 SEM 2 2019/2020en_US
dc.description.abstractAs we all know that solar energy is widely used worldwide as one of the green alternative energy after the wind energy and hydro energy. From the knowledge of photovoltaic (PV) system that we know which produces energy and the underlying dependencies, it is possible to reason about fault. Solar panel is known to have a lifespan of 20-25 years and it is not said that after the lifespan the solar panel is not functioning but the efficiency of the power been absorbing by the solar panel is decreasing to 80% therefore this is why we make the study of fault monitoring in order to save the 20% efficiency being lose while increase the lifespan of the solar panel by detecting the early stage of faulty on the solar panel surface. The aim of this thesis is to investigate the current techniques used for detecting faulty to solar panel. Besides that, classify the types of fault using artificial intelligent techniques is also one of the objective of this study. Thus, collecting data from solar farms with faulty and normal condition solar panel for clear view of the real solar panel fault. A lot of consideration to need to be aware such as the types of solar panel use, type of faulty and also type of faulty monitoring that had in the market. At the end of this project, the goal is to study the solar panel fault monitoring using image processing approach through matlab software.en_US
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21218
dc.language.isoenen_US
dc.subjectfault monitoringen_US
dc.subjectsolar panelen_US
dc.subjectmonitoringen_US
dc.subjectimage processingen_US
dc.titleSolar cell fault monitoring using image processing approach
dc.typeResource Types::text::Final Year Project
dspace.entity.typePublication
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