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A comparative study...
A comparative study on bayes classifier for detecting photovoltaic module visual faults using deep learning features
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- Venkatesh, Naveen (författare)
- Luleå tekniska universitet,Drift, underhåll och akustik,School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India
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- Sugumaran, V. (författare)
- School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India
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- Subramanian, Balaji (författare)
- Department of Mechanical Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203 Chennai, India
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- Josephin, J.S. Femilda (författare)
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkiye; Department of Autotronics, Institute of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, Tamil Nadu, India
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- Varuvel, Edwin Geo (författare)
- Department of Mechanical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkiye
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(creator_code:org_t)
- 2024
- 2024
- Engelska.
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Ingår i: Sustainable Energy Technologies and Assessments. - 2213-1388 .- 2213-1396. ; 64
- Relaterad länk:
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Renewable energy is found to be an effective alternative in the field of power production owing to the recent energy crises. Among the available renewable energy sources, solar energy is considered the front runner due to its ability to deliver clean energy, free availability and reduced cost. Photovoltaic (PV) modules are placed over large geographical regions for efficient solar energy harvesting, making it difficult to carry out maintenance and restoration works. Thermal stresses inherited by photovoltaic modules (PVM) under varying environmental conditions can lead to failure of internal components. Such failures when left undetected impart a number of complications in the system that will lead to unsafe operation and seizure. To avoid the aforementioned uncertainties, frequent monitoring of PVM is found necessary. The fault identification in PVM using essential features taken from aerial images is presented in this study. The feature extraction procedure was carried out using convolutional neural networks (CNN), while the feature selection process was carried out by the J48 decision tree method. Six test conditions were considered such as delamination, glass breakage, discoloration, burn marks, snail trail, and good panel. Bayes Net (BN) and Naïve Bayes (NB) classifiers were utilized as primary classifiers for all the test conditions. Results obtained from the classifiers were compared and the best classifier for fault detection in PVM is suggested.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Annan maskinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Other Mechanical Engineering (hsv//eng)
Nyckelord
- Condition monitoring
- Photovoltaic modules (PVM)
- Fault diagnosis
- Machine learning
- Convolutional neural networks (CNN)
- Visual faults
- Feature extraction
- Operation and Maintenance Engineering
- Drift och underhållsteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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