广东电力2026，Vol.39Issue(4)：40-50,11.DOI:10.3969/j.issn.1007-290X.2026.04.004
基于自适应模态分解与半监督时空融合网络的电力套管故障诊断方法
A Power Bushing Fault Diagnosis Method Based on Adaptive Mode Decomposition and Semi-supervised Spatiotemporal Fusion Network
摘要
Abstract
To improve the effectiveness of feature extraction in bushing fault diagnosis of 220 kV substation GIS equipment and alleviate the difficulty of model training caused by the scarcity of labeled samples,a fault diagnosis methods based on PKO-VMD feature enhancement and a semi-supervised spatiotemporal fusion network is proposed.First,the Pied Kingfisher Optimizer(PKO)algorithm is used to adaptively optimize key parameters of variational mode decomposition,achieving high-precision decomposition and sensitive mode enhancement of non-stationary monitoring signals.Then,a CNN-BiGRU-Attention deep fusion network is constructed to extract local features,bidirectional temporal dependencies,and key discriminant information of multimodal signals.Finally,a semi-supervised learning framework consisting of a supervised term,a consistency constraint term,and an entropy minimization term is introduced to collaboratively utilize a small number of labeled samples and a large number of unlabeled samples to complete model training.Experimental results on the 220 kV substation GIS equipment bushing monitoring dataset show that the proposed method achieves a fault diagnosis accuracy of 96.7％,demonstrating superior overall performance compared to comparative methods;even with only 20％labeled samples,it still maintains a diagnostic accuracy of 94.2％.The results show that the proposed method can effectively improve the accuracy,stability and generalization ability of bushing fault diagnosis for GIS equipment,providing a feasible technical approach for intelligent operation and maintenance of power equipment.关键词
电力套管/故障诊断/特征增强/半监督学习/智能运维Key words
power bushing/fault diagnosis/feature enhancement/semi-supervised learning/intelligent operation and maintenance分类
信息技术与安全科学引用本文复制引用
马志钦,蔡玲珑,廖梓豪,林江,王强..基于自适应模态分解与半监督时空融合网络的电力套管故障诊断方法[J].广东电力,2026,39(4):40-50,11.基金项目
中国南方电网有限责任公司科技项目(GDKJXM20240122) （GDKJXM20240122）
