Abstract:Smart grid, the intelligent system for the power grid, can be defined as a communication and information support platform based on the coordinated development of transmission networks and distributed power grid. It is also a highly integrated system characterized by informatization, automation, and interaction of all voltage levels, including power transmission, transformation, distribution, and dispatch. However, the smart grid is facing the following technical challenges, including the data transmission protocol for power grid monitoring, computing/processing efficiency, as well as detections for information, network attacks, and data anomalies. To this end, a novel data tag-based intelligent power grid monitoring architecture and anomaly detection algorithm is proposed using the Internet of Things and data-tagging technologies. The monitoring framework and anomaly detection are firstly designed based on data tagging for the intelligent power grid, including data labeling method, and task categories for monitoring-related big data. Subsequently, the anomaly detection paradigm is designed to support the monitoring framework, in which the sparsification/simplification and detection algorithms are proposed based on data labels. To validate the proposed framework, the experimental and simulation configurations are provided to conduct the result comparison over selective baselines, in terms of the accuracy and recall under different sequential data dimensions, anomaly data amounts, as well as running time for different test data amounts. Experimental results demonstrate that, compared to the baselines, the proposed approach achieves over 80% accuracy and 82% recall with the increase of the sequential data dimensions. In addition, a similar performance improvement in terms of accuracy and recall can also be obtained for different anomaly data amounts. As to the running time, the proposed approach also harvests a reduction of 2.0～3.0 seconds for different test data amounts.