With the continuous expansion and increasing topological complexity of modern power grids, achieving high-precision fault localization under sparse measurement conditions has become a core challenge in the operation and maintenance of smart grids. Existing methods based on deep graph networks generally face complex spatiotemporal coupling between fault types and fault localization. To address this, this paper proposes a recognition method for fault localization based on sparse measurements and spatial configuration. A reinforcement learning algorithm with a Checking-Action mechanism, termed DQN-CA, is adopted to identify optimal PMU installation buses. In parallel, a dual-path spatio-temporal multi-task graph convolutional network, termed ST-MTGCN, is developed to decouple fault-type-related features from topology-sensitive fault-Localization features through a global feature dimensionality-reduction path and a K-hop spatial graph convolution path, thereby accomplishing the fault localization task. Experimental results on the IEEE 39-bus system show that ST-MTGCN achieves 99.68% fault type accuracy, 89.94% fault localization accuracy, and 88.62% accuracy for 185 joint fault type-Localization classes under the OPT13 configuration. Comparative experiments, PMU configuration sensitivity analysis, and ablation studies further demonstrate the effectiveness of the proposed framework under sparse measurement conditions.
Wu et al. (Tue,) studied this question.