基于可泛化图知识蒸馏的油浸式电力变压器故障检测OA

Dissolved Gas Analysis(DGA)aims to identify potential fault types by monitoring the dissolved gases in insulating oil.However,existing DGA methods exhibit limited performance because of the constraints imposed by the scarcity of labeled data.To address this issue,a novel Graph Knowledge Distillation method(GKDG)is proposed to enhance the accuracy and efficiency of the DGA.It employs a dual-view graph construction strategy to obtain additional supervision from sample neighborhoods,aggregating information directly from other samples through propagation.Furthermore,knowledge from the teacher Graph Neural Network(GNN)is distilled into the student GNN model,ensuring that the student model can effectively capture and interpret the complex relationships among the dissolved gases.Additionally,to align the student and teacher graphs in the embedding space,multiple types of knowledge are introduced,thereby enhancing the learning capability of the student model and enabling it to learn better from the teacher model.The experimental results validate the effectiveness of the GKDG in improving the DGA performance,providing strong support for the maintenance and fault detection of power equipment.