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Adaptive Deep Belief Networks and LightGBM-Based Hybrid Fault Diagnostics for SCADA-Managed PV Systems : A Real-World Case Study

author
statement of authorship
Kull, Karl; Khan, Muhammad Amir; Asad, Bilal; Naseer, Muhammad Usman; Kallaste, Ants; Vaimann, Toomas
source
special issue
Advanced Condition Monitoring and Fault Analysis in Industrial Electronics
publisher
journal volume number month
vol. 14, 18
year of publication
pages
art. 3649
url
ISSN
2079-9292
Open Access
Open Access
scientific publication
teaduspublikatsioon
language
Inglise
subject term
keyword
artificial intelligence
back propagation neural network
Bayesian optimization
deep learning
hybrid fault detection
I–V curves
machine learning
maximum power point tracking
photovoltaic fault detection algorithms
photovoltaic systems
PV fault classification
SCADA
Kull, K., Khan, M. A., Asad, B., Naseer, M. U., Kallaste, A., Vaimann, T. Adaptive Deep Belief Networks and LightGBM-Based Hybrid Fault Diagnostics for SCADA-Managed PV Systems : A Real-World Case Study // Electronics (2025) vol. 14, 18, art. 3649. https://doi.org/10.3390/electronics14183649