Abstract：With the continuous advancement of the Carbon Peaking and Carbon Neutrality Goal and the construction of new power systems, the high penetration of distributed generation, power electronic devices/loads makes the transition from traditional distribution networks to new distribution systems an inevitable trend. Under complex operational scenarios such as renewable energy output fluctuations and frequent topology reconfigurations, improving the accuracy and interpretability of existing AI-based fault diagnosis and location schemes for distribution networks has become an urgent challenge. To address this issue, this paper proposes a spatiotemporal graph-based fault section location and interpretability analysis method for distribution networks. Firstly, generators (or buses), loads, and other components in the distribution network are abstracted as nodes, and each line is abstracted as an edge. Based on the spatial relationships of measurement points, a spatiotemporal graph structure dataset for the distribution network is constructed, integrating temporal continuity and spatial integrity. Secondly, a fault location model based on spatio-temporal graph convolution (STGCN) is developed. This model extracts implicit spatiotemporal features through a temporal information capture basic block (TIC-Block) and a spatial feature perception basic block (SFP-Block). The mapping relationship between high-dimensional features and fault sections is then depicted via a segment classification module. Then, an interpretability analysis module is designed, generating a spatial weight map of measurement points. This enables post-hoc interpretability analysis of the model's decision basis and internal mechanisms, thereby verifying the fault location results. Finally, a typical distribution system simulation model is constructed in Matlab/Simulink to validate the proposed method, the following conclusions were drawn: (1) The proposed STGCN-based fault location scheme achieves an accuracy of 94.74%, outperforming single-function schemes such as CNN, LSTM, and GCN. This highlights the model's ability to effectively extract fault features from the spatiotemporal graph structure data of distribution networks. (2)Under complex operating scenarios such as renewable energy output fluctuations, noise interference, and topology reconfigurations, the proposed fault location scheme demonstrates strong generalization capability, providing an effective solution for fault location in dynamically operating distribution networks. (3) The interpretability analysis module designed in this paper quantifies the contribution of each measurement point to the output results by reconstructing the spatial weight map, thereby verifying the rationality of fault section location. The results show that compared to existing similar methods, the proposed scheme maintains excellent generalization capability under complex operational scenarios such as distributed generation variations, noise interference and data loss, and topology reconfigurations. It demonstrates high location accuracy, strong robustness, and good interpretability, offering a new research perspective for enhancing the interpretability of fault location schemes in distribution networks.
刘畅宇, 王小君, 张大海, 刘曌, 尚博阳, 窦嘉铭. 融合时空图信息的配电网故障区段定位及可解释性分析方法[J]. 电工技术学报, 2026, 41(5): 1623-1636.
Liu Changyu, Wang Xiaojun, Zhang Dahai, Liu Zhao, Shang Boyang, Dou Jiaming. Research on Fault Section Location Method in Distribution Networks Integrating Spatio-Temporal Graph Information and Interpretability. Transactions of China Electrotechnical Society, 2026, 41(5): 1623-1636.
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2026, Vol. 41 
