用视觉化方法区分开关电压下的六类局部放电源。
Classification of Single and Mixed Partial Discharges under Switching Voltage Using an AWA-CNN Framework
- 提取脉冲幅值、宽度、面积，生成颜色编码的可视化模式。
- 基于CNN分类准确率超96%，远高于随机森林的73.33%。
- 适合电力设备局部放电故障诊断场景，尤其开关电源环境。
快速开关电力电子设备的普及使开关电压激励下的局部放电（PD）分析愈发重要，但因放电活动集中于电压跳变点，难度高于正弦激励。本文提出一种幅值-宽度-面积（AWA）模式表示法，用于源导向的开关电压下PD分析。将时域PD脉冲以幅值、宽度和面积表征，并映射为视觉模式：幅值与面积决定坐标轴，宽度由颜色编码。生成的AWA模式可区分六类单一及混合放电源：电晕、内部、表面、电晕+内部、电晕+表面、内部+表面。对比了随机森林基线及InceptionV3、ResNet-18两种卷积神经网络模型。结果表明，AWA模式呈现可区分的源相关分布，基于CNN的分类测试准确率超过96%，显著优于随机森林的73.33%。证明了AWA模式在开关电压下多类放电源分类中的有效性。
原文摘要 · Abstract (English)
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.
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