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

autor
vastutusandmed
Kull, Karl; Khan, Muhammad Amir; Asad, Bilal; Naseer, Muhammad Usman; Kallaste, Ants; Vaimann, Toomas
allikas
ajakirja erinumber
Advanced Condition Monitoring and Fault Analysis in Industrial Electronics
kirjastus/väljaandja
ajakirja aastakäik number kuu
vol. 14, 18
ilmumisaasta
leheküljed
art. 3649
leitav
ISSN
2079-9292
Open Access
Open Access
teaduspublikatsioon
teaduspublikatsioon
keel
English
märksõna
võtmesõna
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