Distribution Network Anomaly Detection Based on Graph Contrastive Learning

Abstract

As smart grid development advances, anomaly detection and verification of distribution network topology have become crucial for ensuring reliable power supply. Existing methodologies face two primary challenges: they often overlook the contextual relationships among multiple device measurements, leading to increased false positive rates, and they rely heavily on numerous task-specific labels, limiting their applicability in real-world few-shot scenarios. To address these challenges, this paper presents DNT-GCL, a novel few-shot anomaly detection technique based on graph contrastive learning. DNT-GCL utilizes a heterogeneous graph to construct a comprehensive grid topology that integrates feeder topology data with multi-source measurement data, effectively capturing detailed attribute information of physical quantities within the network. Additionally, DNT-GCL implements two adversarial data augmentation strategies to generate diverse positive and negative instance pairs, thereby enhancing the model’s robustness against confusion and adversarial attacks. Finally, a Graph Convolutional Network (GCN) is employed as a contrastive learning discriminator to extract higher-order semantic information in a self-supervised manner. Experimental results indicate that DNT-GCL achieves an accuracy improvement of at least 8.75% over baseline methods and demonstrates remarkable performance in few-shot scenarios.