Novel metric-based meta-learning model for few-shot diagnosis of partial discharge in a gas-insulated switchgear

Data-driven diagnosis methods have been systematically investigated for the diagnosis of gas-insulated switchgear (GIS) partial discharge (PD). However, because of the scarcity of samples on-site, an operational gap exists between the diagnostic methods and their actual application. To settle this issue, a novel metric-based meta-learning (MBML) method is proposed. First, a hybrid self-attention convolutional neural network is constructed for feature extraction and trained through supervised learning. Then, the episodic MBML is used to train other parts, and the metric classifier is employed for diagnosis. The proposed MBML exhibits an accuracy of 93.17% under 4-way 5-shot conditions, which is a significant improvement over traditional methods. When the number of support sets is small, the benefits of MBML are more prominent, providing a viable solution for the on-site diagnosis of PD in GISs. •A novel MBML method is proposed for few-shot GIS PD diagnosis on-site.•A hybrid self-attention CNN is developed to extract the discriminative features.•A multi-level metric is introduced to avoid effective information loss.•With comparative average loss and episodic training, the model converges stably.•The method proves high-accuracy and robust diagnosis for few-shot GIS on-site.