| Title | Anomaly Detection Model of Power Grid Data Based on STL Decomposition |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zhang, Cuicui, Sun, Jiali, Lu, Ruixuan, Wang, Peng |
| Conference Name | 2021 IEEE 5th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) |
| Date Published | oct |
| Keywords | anomaly detection, Automation, cloud computing, composability, compositionality, Computer architecture, Conferences, Data analysis, data center, data centers, decomposition, Metrics, pubcrawl, Semantics, STL decomposition method, Training |
| Abstract | This paper designs a data anomaly detection method for power grid data centers. The method uses cloud computing architecture to realize the storage and calculation of large amounts of data from power grid data centers. After that, the STL decomposition method is used to decompose the grid data, and then the decomposed residual data is used for anomaly analysis to complete the detection of abnormal data in the grid data. Finally, the feasibility of the method is verified through experiments. |
| DOI | 10.1109/ITNEC52019.2021.9587107 |
| Citation Key | zhang_anomaly_2021 |
Anomaly Detection Model of Power Grid Data Based on STL Decomposition