Background Information Detection in Substations Based on Anomaly Detection

Abstract

The substations are important parts of modern electrical grids. In this sense, it is necessary to detect the anomaly and problems in it. In this paper, we study on the information detection in substations based on traditional anomaly detection algorithms. The data flow from the background information is represented by feature vectors. And those from the historical data are used to build the background references. Afterward, the feature vector of the input data flow is examined using the anomaly detection algorithm. Based on the results, the anomaly in the background information in the substations can be found and located. Then, some high-precision identification algorithms can be further employed to recognition the type of the problems. In this way, the problems occurred in the substations can be found and solved in time.