Multi-Expert Collaboration Based Information Graph Learning for Anomaly Diagnosis in Smart Grids

Accurate and reliable fault diagnosis is critical for secure operation in complex smart power systems. While graph neural networks show promise for this task, existing methods often neglect the long-tailed distribution inherent in real-world grid fault data and fail to provide reliability estimates for their decisions. To address these dual challenges, we propose a novel multi-expert collaboration uncertainty-aware power fault recognition framework with cross-view graph learning. Its core innovations are two synergistic modules: (1) The infographics aggregation module tackles the long-tail problem by learning robust graph-level representations. It employs an information-driven optimization loss within a contrastive graph architecture, explicitly preserving global invariance and local structural information across diverse (including rare) fault states. This ensures balanced representation learning for both the head and tail classes. (2) The multi-expert reliable decision module addresses prediction uncertainty. It trains individual expert classifiers using the Dirichlet distribution to explicitly model the credibility (uncertainty) of each expert’s decision. Crucially, a complementary collaboration rule based on evidence theory dynamically integrates these experts. This rule generates active weights for expert participation, prioritizing more certain experts and fusing their evidence to produce a final decision with a quantifiable reliability estimate. Collaboratively, these modules enable reliable diagnosis under data imbalance: The Infographics Module provides discriminative representations for all fault types, especially tail classes, while the Multi-Expert Module leverages these representations to make decisions with explicit uncertainty quantification. This synergy significantly improves both the accuracy and the reliability of fault recognition, particularly for rare or ambiguous grid conditions. Ultimately, extensive experiment evaluations on the four datasets reveal that the proposed method outperforms the state-of-the-art methods in the fault diagnosis of smart grids, in terms of accuracy, precision, f score and recall.