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.
Highlights
The safety of modern electric grids is closely related to people’s daily life
For each data flow from the background information, it is first converted to feature vectors
It is assumed that the anomaly detection method could operate with high efficiency to find and locate the abnormal samples in the large amount of data flows
Summary
The safety of modern electric grids is closely related to people’s daily life. As an important part of the electrical system, the protection of substations are important tasks to manage the smooth operating the whole system [1-3]. It is necessary to research on the information detection algorithms about the substations In this way, the occurred problems can be found and handled in time to avoid the corruption of the whole system. We research on the background information detection method in substations based on traditional anomaly detection algorithms. The anomaly detection tends to find the information with great differences with the background. The anomaly detection algorithms were successfully applied to image processing, target detection, etc. For each data flow from the background information, it is first converted to feature vectors. When the differences are higher than a preset threshold, the feature vector is judged to be an anomaly one and the corresponding data flow is assumed to be an anomaly one. Corresponding measures can be taken to deal with these problems
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
