CNNs achieve 96% accuracy classifying partial discharge using novel AWA patterns

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

影响 Introduces a new visual representation for PD pulses, enabling higher accuracy classification of electrical faults using CNNs.

排序理由 The cluster contains an academic paper detailing a new methodology and benchmark results for classification using machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• Amplitude-Width-Area (AWA)
• Anindya Bijoy Das
• InceptionV3
• Random Forest
• ResNet-18

• 论文
• 其他

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