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

Frontiers in Energy Research

Yuhang Zhang¹  Xing He¹  Fan Yang¹  Qian Ai¹  Zenan Ling³  Robert C. Qiu⁴ 

[1] Department of Electrical Engineering, State Energy Smart Grid Research and Development Center, Shanghai Jiaotong University, Shanghai, China;Key Laboratory of Machine Perception (MoE), School of EECS, Peking University, Beijing, China;Pazhou Laboratory, Guangzhou, China;School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan, China;

关键词: distribution network;    data-driven;    event detection and localization;    event classification;    invertible neural network;    pseudo labels;   

DOI  :  10.3389/fenrg.2022.858665

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown