AWARE uses waveform signatures to detect and classify early-stage grid faults, enabling proactive intervention. The system combines physics-based models with AI/ML to interpret subtle electrical ...

CNN and random forest model to detect multiple faults in bifacial PV systems, including dust, shading, aging, and cracks. Using simulated I-V curves and a 180-day synthetic dataset, the model achieved ...

A group of researchers led by the University of Sharjah in the UAE proposed to use the convolutional neural network (CNN) technique to detect temperature and shading-induced faults in PV modules. CNN ...

According to the analysis, deep learning architectures such as Long Short-Term Memory (LSTM) networks and hybrid CNN-LSTM ...

IC manufacturers are increasingly relying on intelligent data processing to prevent downtime, improve yields, and reduce scrap. They are integrating that with fault detection and classification (FDC) ...