Event Detection and Identification in Distribution Networks Based on Invertible Neural Networks and Pseudo Labels

Abstract

Anomalous event detection and identification are important to support situational awareness and security analysis in power grids. Particularly, the distribution network is with complicated topology, variable load behaviors, and integration of nonlinear distributed generators (DGs), which is difficult to implement complete modeling mathematically. With the deployment of advanced measurement devices such as μPMUs in distribution networks, massive data containing rich system status information becomes available. In this paper, a framework for event detection, localization, and classification is studied to extract event features from measurements in distribution networks. Specifically, a method based on an invertible neural network (INN) is employed to model the complex distributions of normal-state measurements offline in a flexible way. It then establishes explicit likelihoods as the indicator to enable real-time event detection. Furthermore, a Jacobian-based method is utilized for spatial localization. Finally, as the events in practical power grids are mostly recorded unlabeled, the pseudo label (PL) based approach, superior in the separating ability for events under a low labeling rate, and is used to implement event classification. Several typical types of events simulated in the IEEE 34-bus system and real-world cases in a low-voltage system verify the effectiveness and superiorities of the framework.