【目的】配电网单相接地故障初期特征微弱、信噪比较低，传统故障检测方法在标签数据样本有限时存在检测精度低和泛化能力不足的问题。为解决此问题，本文设计了一种融合Transformer与时间卷积网络(TCN)的TransTCN半监督协同学习框架。【方法】首先，采用改进互补集合经验模态分解(ICEEMD)方法对故障零序电流信号进行自适应模态分解，筛选最优特征分量；然后，通过少量标签数据样本初始化模型训练，并基于高置信度伪标签生成机制扩充无标签数据集，结合权重自适应分配的损失函数实现模型参数迭代优化；最后，基于PSCAD构建10 kV配电网单相接地故障模型，对所提TransTCN半监督模型在不同接地电阻、故障初始角及运行工况下的检测性能进行了验证。【结果】在有标签数据比例仅为15%的条件下，所提TransTCN半监督模型对弱特征单相接地故障的识别准确率高达95.31%。【结论】TransTCN半监督模型在弱特征提取和小样本学习场景下具有明显优势，在故障识别精度、收敛稳定性及跨工况泛化能力等方面均表现良好，具备一定的工程应用价值。

[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.

南方电网公司科技项目(GDKJXM20240450)