An Intelligent Master Station Abnormality Monitoring and Deep Diagnosis Framework Based on Residual Networks and Rule Templates

With the rapid development of smart grids, the intelligent metering master station has become a critical control node whose operational stability directly affects data acquisition and power scheduling. However, due to the complexity of operating environments, the master station is prone to various types of abnormalities. Traditional rule-based detection methods struggle to handle diverse and implicit abnormal patterns, while deep learning methods, although powerful in modeling capabilities, often lack interpretability.To address these issues, this paper proposes a deep diagnosis framework named Residual and Rule Template-based Deep Diagnosis (RaRT-Diag), which integrates residual networks with expert-defined rule templates. The framework first uses a residual network to extract features from master station data and perform initial abnormality detection. Then, it employs rule templates derived from expert knowledge to refine the classification and conduct logical reasoning. This two-stage design realizes a collaborative mechanism of “coarse recognition[Formula: see text] [Formula: see text] [Formula: see text]fine diagnosis”, enhancing both detection accuracy and interpretability. Experiments conducted on real-world datasets demonstrate that RaRT-Diag outperforms traditional rule systems and standalone deep models in multi-type anomaly detection tasks, achieving higher accuracy and F1-score, and exhibiting strong generalization and practical value.