Abstract: To reduce redundant alarms in the power monitoring system and improve the efficiency and accuracy of alarm analysis, an alarm denoising method based on rule-statistical-transformer three-stage fusion is developed in this study. Efficient and accurate false alarm identification is achieved through a layered filtering mechanism. First, false alarms with known patterns are rapidly screened using the rule matching engine, completing initial data cleaning. Second, duplicate alarms and periodic noise are detected through statistical analysis, accomplishing secondary noise reduction. Subsequently, in-depth semantic analysis of remaining alarms is performed by the transformer model, with complex false alarm patterns being captured via the attention mechanism. Finally, data are collected from a certain provincial company's network security platform for effect verification. The experimental results demonstrates that the proposed method significantly improves detection efficiency while maintaining accuracy. The execution time is reduced by over 15% for large-scale datasets.

Key words: alert denoising, Transformer, three-stage fusion, rule-based matching, statistical analysis, smart grid