Anomaly detection method for power grid indicator data based on deep learning
Sheng Zhenming
Guo Yaosong
Liu Chao
Cheng Yang
Han Xiao
Fang Wei
Abstract：Objectives Time series anomaly detection based on deep learning has plays a key role in intelli-gent operation and maintenance scenarios such as power system operations,equipment fault detection,and power grid fault monitoring.While existing methods have achieved notable success,they often focus on fixed time windows and overlook correlations between different feature dimensions of the time series,which can lead to false positives and reduced detection accuracy.Methods This paper proposes a Temporal-Feature Fusion Anomaly Transformer(TFFAT)model for unsupervised anomaly detection in multivariate power grid indicator data.TFFAT leverages a graph attention mechanism to capture complex dependencies from both the temporal and feature dimensions in parallel.It employs an anomaly transformer to process the fused hidden features and compute anomaly scores.Results Experimental results on three publicly available time series anomaly detection datasets show that TFFAT achieves detection accuracies of 89.73%,92.12%,and 97.14%,respectively,significantly outperforming existing benchmark methods.Conclusions TFFAT effectively captures interdependencies across temporal and feature dimensions,enabling more accu-rate detection of anomalies in time series data.It demonstrates strong potential for application in power grid operation and maintenance,significantly improving fault detection accuracy,reducing false positives,and enhancing the stability and reliability of the power grid.
Keywords：deep learningpower grid dataanomaly detectionpower grid operation and maintenancepower grid monitoringtime series
Publication Date：2025-07-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 59-65 )