Gas-insulated switchgear (GIS) plays a critical role in ensuring the reliability of power systems

but partial discharge (PD) is a primary cause of failures within GIS equipment. Traditional PD diagnostic methods rely heavily on laboratory data

which differ significantly from that under the complex conditions of field data

leading to a marked drop in recognition accuracy when they are applied to field PD diagnosis. This study addresses the challenge by integrating field data into the training process

utilising a deep transfer learning approach that combines laboratory and field data to improve diagnostic accuracy for GIS PD. The research collected PD data from laboratory models representing five defect types and field data gathered from operational GIS equipment. A deep residual network (ResNet50) was pretrained using laboratory data and fine-tuned with field data through deep transfer learning to optimise the recognition of PD in field conditions. The results show that the proposed model achieves a significantly higher recognition accuracy (93.7%) for field data compared to traditional methods (60%–70%). The integration of deep transfer learning ensures that both low-dimensional general features from laboratory data and high-dimensional specific features from field data are effectively utilised. This research significantly contributes to improving the diagnostic accuracy of PD in GIS under field conditions

providing a robust method for defect detection in operational equipment.