Machine learning-based fault detection technique for bifacial PV

A UAE research team developed a hybrid 1D-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 up to 100% accuracy in general state detection and 97.6% in specific fault classification.A research team led by the University of Sharjah in the United Arab Emirates has developed a novel machine learning approach for fault detection in bifacial PV systems. The method combines a one-dimensional convolutional neural network (1D-CNN) with the random forest ...

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