Intelligent Distribution Network Operation and Anomaly Detection Based on Information Technology

In response to the current challenges of limited monitoring methods and underutilization of data in distribution networks, this paper proposes a study on intelligent distribution network operation and anomaly detection using information technology. This article begins by analyzing the technical characteristics and data flow within the current power supply enterprise distribution network scheduling support system. It selects extensive historical telemetry data as the subject of research and employs the C-means fuzzy clustering algorithm to identify distribution line load patterns and conduct load prediction. A mismatch degree index, taking into account membership degree and Euclidean distance factors, serves as the criterion for assessing line faults. A 12 kV distribution line (L) in a certain area was chosen and tested using real operational data to see if the proposed big data-based method for monitoring distribution network faults works. The test results show that this study can correctly find patterns in distribution line loads, reliably find line faults, and, to some extent, lessen the problems that come up when loads change normally. The application results reveal that this study offers a straightforward and practical monitoring approach that effectively assesses the fault status of distribution networks that cannot be monitored using existing methods.