计算机工程2026，Vol.52Issue(9)：449-456,8.DOI:10.19678/j.issn.1000-3428.0070580
基于可泛化图知识蒸馏的油浸式电力变压器故障检测
Fault Detection in Oil-Immersed Power Transformers Based on Generalizable Graph Knowledge Distillation
摘要
Abstract
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.关键词
油浸式电力变压器/故障检测/溶解气体分析/图神经网络/知识蒸馏Key words
oil-immersed power transformers/fault detection/Dissolved Gas Analysis(DGA)/Graph Neural Network(GNN)/knowledge distillation分类
信息技术与安全科学引用本文复制引用
张瑞佳,马慧芳,张映月,彭生江..基于可泛化图知识蒸馏的油浸式电力变压器故障检测[J].计算机工程,2026,52(9):449-456,8.基金项目
甘肃省重点基础研究项目(24JRRA123) （24JRRA123）
甘肃省产业支撑项目(2022CYZC11). （2022CYZC11）
