关键词: 异常检测, 异构信息网络, 相似性学习, 图注意力网络, Transformer

Abstract:

Anomaly detection has an important impact on the development of the electric power industry, and how to detect anomalies based on large-scale power data is a research hotspot. At present, most researches use clustering or neural network to detect anomalies. But these methods ignore the potential relationship between the data and miss some specific important information, and do not fully exploit the potential value of the data. Therefore, an abnormal detection model based on graph attention and transformer is proposed. The model first constructs a heterogeneous information network based on the power data (mainly including user ID, meter ID, user type, electrical current, voltage, power, etc.) collected in the data center; then, in order to reduce the model parameters and avoid the phenomenon of overfitting, on the basis of the graph convolutional network (GCN) model, a non-negative matrix factorization (NNMF) method is introduced to perform similarity learning; finally, a graph attention network (GAT) and Transformer are jointly used to capture the correlation relationships between data, thus improving the detection accuracy. The validation analysis is carried out based on the power data of a region in China. The experimental results show that the proposed method can effectively perform anomaly detection.

Key words: abnormal detection, heterogeneous information network, similarity learning, graph attention, Transformer