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

TL;DR

This paper introduces an AWA-CNN framework for classifying single and mixed partial discharges under switching voltage, using a novel visual pattern representation that improves accuracy over traditional methods.

Contribution

The study proposes a new AWA pattern representation for PD analysis and demonstrates its effectiveness with CNN models achieving over 96% accuracy.

Findings

AWA patterns are source-dependent and distinguishable.

CNN models outperform Random Forest in classification accuracy.

The method effectively classifies multiple PD source conditions.

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.