Classification of Single and Mixed Partial Discharges under Switching Voltage Using an AWA-CNN Framework

2026-05-20

AI Summary

This study addresses the challenge of partial discharge (PD) identification under switching voltage excitation, where discharges concentrate around voltage transitions and are significantly harder to classify than under sinusoidal conditions. To tackle this, the authors propose an amplitude–width–area (AWA) visualization method that maps time-domain pulse features into a two-dimensional image: amplitude and area serve as spatial coordinates, while pulse width is encoded by color, effectively revealing distinct distribution patterns of different PD sources. Leveraging this representation, convolutional neural networks—including InceptionV3 and ResNet-18—are employed to achieve high-accuracy classification of six single- and mixed-source PD types. Experimental results demonstrate a classification accuracy exceeding 96%, substantially outperforming a Random Forest baseline (73.33%), thereby validating the effectiveness and superiority of the proposed approach for multi-class PD identification in complex switching voltage environments.

Abstract

The growing use of fast-switching power electronics has made partial discharge (PD) analysis under switching-voltage excitation increasingly important, yet more challenging than under sinusoidal conditions due to activity concentrated at voltage transitions. This work presents an Amplitude-Width-Area (AWA) pattern representation for source-oriented PD analysis under switching-voltage excitation. In the proposed method, time domain PD pulses are characterized using pulse amplitude, width, and area, and mapped into a visual pattern where amplitude and area define the coordinate axes and width is encoded by color. The generated AWA patterns are used to distinguish six single and mixed PD source conditions: corona, internal, surface, corona+internal, corona+surface, and internal+surface. To evaluate the classification capability of the proposed representation, a Random Forest baseline and two Convolutional Neural Network (CNN) models, InceptionV3 and ResNet-18, are compared. The AWA patterns show distinguishable source-dependent distributions, and CNN-based classification achieves testing accuracy above 96%, compared with 73.33% for Random Forest. The results indicate that AWA patterns provide a visual representation of PD pulses suitable for multi-class PD source classification under switching-voltage excitation.

Problem

• partial discharge
• switching voltage
• source classification
• mixed PD
• AWA pattern

Innovation

• AWA pattern
• partial discharge classification
• switching voltage
• CNN-based diagnosis
• visual representation