Classification of Single and Mixed Partial Discharges under Switching Voltage Using an AWA-CNN Framework
Researchers have developed a novel Amplitude-Width-Area (AWA) pattern representation to analyze partial discharge (PD) pulses under switching-voltage excitation. This method maps PD pulses into visual patterns using amplitude, width, and area, enabling the distinction of six different PD source conditions. Convolutional Neural Network (CNN) models, specifically InceptionV3 and ResNet-18, achieved over 96% accuracy in classifying these sources, significantly outperforming a Random Forest baseline. AI
IMPACT Introduces a new visual representation for PD pulses, enabling higher accuracy classification of electrical faults using CNNs.