基于TransTCN半监督模型的配电网单相接地故障检测方法研究OA

[Objective]The initial feature of single-phase grounding faults in distribution networks is weak,with a low signal-to-noise ratio.Traditional fault detection methods suffer from low detection accuracy and insufficient generalization capability when labeled data samples are limited.To address this issue,this paper proposes a TransTCN semi-supervised collaborative learning framework that integrates Transformer and temporal convolutional network(TCN).[Methods]Firstly,the improved complementary ensemble empirical mode decomposition(ICEEMD)method was employed to perform adaptive mode decomposition on fault zero-sequence current signal,thereby selecting the optimal feature components.Secondly,model training was initialized with a small number of labelled data sample,and the unlabeled dataset expanded through a high-confidence pseudo-label generation mechanism,and combined with a loss function featuring weight-adaptive allocation to achieve iterative optimization of model parameters.Finally,a single-phase grounding fault model for a 10 kV distribution network was constructed using PSCAD.The detection performance of the proposed TransTCN semi-supervised model was validated under varying grounding resistances,initial fault angles,and operational conditions.[Results]Under conditions where labeled data constituted merely 15% of the dataset,the proposed TransTCN semi-supervised model achieved an identification accuracy of 95.31%for weak feature single-phase grounding fault.[Conclusion]TransTCN semi-supervised model has significant advantages in weak feature extraction and few-sample learning scenarios.It performs well in terms of fault identification accuracy,convergence stability,and cross-condition generalization ability,and has certain engineering application value.