Research on Machine Learning-Based Business Anomaly Detection in Power Dispatch Automation Systems

This article proposes a hybrid machine learning model for detecting abnormal events in the power dispatch automation system. The model extracts features from both temporal and spatial dimensions to enhance algorithm performance and accurately locate anomalies. An unsupervised anomaly scoring mechanism is introduced to improve the detection of hidden anomalies. The study is divided into four parts: first, a time-space feature fusion model is developed for more accurate abnormal point localization; second, the model’s real-world effectiveness is validated through deployment in grid fault detection; third, dynamic fusion of time-series and high-dimensional spatial features demonstrates strong adaptability, with potential for applications in federated learning and data privacy protection; finally, it discusses the need for improvement in the model’s generalization ability when handling extreme cases.