SHENG Z M, GUO Y S, LIU C, et al. Anomaly detection method for power grid indicator data based on deep learning [J]. Journal of Henan Polytechnic University (Natural Science) , 2025, 44(4): 59-65.

doi: 10.16186/j.cnki.1673-9787.2024070038

Received: 2024/07/09

Revised: 2024/10/08

Published: 2025/06/19

Anomaly detection method for power grid indicator data based on deep learning

Sheng Zhenming1, Guo Yaosong1, Liu Chao1, Cheng Yang1, Han Xiao1, Fang Wei2

1.NARI-TECH Nanjing Control Systems Co.， Ltd.， Nanjing  211106， Jiangsu， China；2.School of Computer Science， Nanjing University of  Information Science & Technology， Nanjing  210044， Jiangsu， China

Abstract: Objectives Time series anomaly detection based on deep learning has plays a key role in intelligent 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 accurate 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. 

Key words: deep learning; power grid data; anomaly detection; power grid operation and maintenance; power grid monitoring; time series