GIS partial discharge diagnosis method under sample imbalance conditions
Year: 2025
Type: article
Abstract: Gas-insulated switchgear (GIS) partial discharge (PD) diagnosis is essential for ensuring the stable operation of modern power systems. Existing PD diagnostic methods are predominantly based on balanced datasets and still have room for improvement in feature extraction and recognition speed. This study proposes a PD diagnostic model that integrates frequency channel attention with the LeViT architecture and trained using the logit adjustment class weight focal loss function (LCF) to enhance the diagnostic performance of GIS PD spectrograms. Firstly, the phase-resolved partial discharge (PRPS) of GIS extracts channel features from low-frequency components through the frequency efficient channel attention (FECA) mechanism. Next, the extracted feature maps are fed into the LeViT model and trained using the LCF loss function to classify PD types. The experimental results demonstrate that the LeViT-FECA-LCF model achieves a recognition accuracy of 99.5%, showing improvements of 1.0%, 1.5%, 1.5%, and 2.5% over EfficientNet-B0, MobileNetV3, ShuffleNetV2, and LeViT, respectively. These improvements are achieved while maintaining a relatively low inference time and computational cost. Ablation studies further confirm that each module contributes positively to the overall performance of the model. Compared with existing methods, the proposed model exhibits superior recognition accuracy. Furthermore, this approach effectively extracts PD PRPS spectrogram features, improving PD identification accuracy and providing new insights for GIS equipment health management.
Authors: Tianyu Hu, Hongzhong Ma, Dawei Duan, Yuan Dong
Institutions: Hohai University, Anhui Science and Technology University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,920
Awards: G3056844793, G5461937895
OpenAlex ID: W4411133445