电力系统保护与控制2026，Vol.54Issue(15)：60-71,12.DOI:10.19783/j.cnki.pspc.260043
基于MHSA时-空域特征融合的金属氧化物避雷器故障诊断方法研究
Research on fault diagnosis method for metal oxide arresters based on MHSA temporal-spatial feature fusion
摘要
Abstract
To address the limitations of insufficient feature representation in single-modal approaches and the relatively high computational complexity of dual-stream fusion models in fault diagnosis of metal oxide arresters(MOAs),a temporal-spatial feature fusion diagnosis method based on the multi-head self-attention(MHSA)mechanism is proposed.First,partial discharge(PD)signals under four operating states are collected,and temporal samples are constructed using Hamming-window segmentation.Then,the signals are mapped into two-dimensional images through symmetrized dot pattern(SDP)transformation.In the proposed method,a bidirectional gated recurrent unit(Bi-GRU)is used to extract temporal features from PD signals,while EfficientNet-B0 is employed to extract spatial features from SDP images.MHSA is then introduced to achieve dynamically weighted fusion,and fault identification is finally performed using a Softmax classifier.Experimental results show that the proposed method achieves a diagnostic accuracy of 98.79%on the test set,with an average loss of 0.0126 and floating-point operations(FLOPs)of 623 M.Compared with single temporal-feature,single spatial-feature,and simple feature-concatenation fusion models,the accuracy is improved by approximately 7.22%,6.08%,and 2.36%,respectively.Feature contribution weight analysis further shows that the contribution weights of the temporal and spatial features are 38.12%and 61.87%,respectively,indicating their complementary roles in MOA fault diagnosis.关键词
金属氧化物避雷器/故障诊断/对称点模式变换/特征融合/EfficientNet-B0模型Key words
metal oxide arrester/fault diagnosis/symmetrized dot pattern transformation/feature fusion/EfficientNet-B0 model引用本文复制引用
张毓辕,周强,李清华,姜瑞敏,刘东矗,王卓一,雷辉兵..基于MHSA时-空域特征融合的金属氧化物避雷器故障诊断方法研究[J].电力系统保护与控制,2026,54(15):60-71,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.62541319). 国家自然科学基金项目资助(62541319) （No.62541319）
陕西省重点研发计划项目资助(2024GX-YBXM-544) （2024GX-YBXM-544）
西安市科技计划项目资助(24GXFW0005) （24GXFW0005）
