CONGO²: Scalable Online Anomaly Detection and Localization in Power Electronics Networks

Journal Article · 13 January 2022 · IEEE Internet of Things Journal (Online)

DOI: https://doi.org/10.1109/jiot.2022.3143123 · OSTI ID:1980405

Yu, Jun  ^[1]; Cheng, Huimin ^[2]; Zhang, Jinan  ^[2]; Li, Qi  ^[2]; Wu, Shushan  ^[2]; Zhong, Wenxuan ^[2]; Ye, Jin  ^[2]; Song, Wenzhan  ^[2]; Ma, Ping  ^[2]

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1. Beijing Institute of Technology (China); University of Georgia
2. University of Georgia, Athens, GA (United States)

Rapid and accurate detection and localization of electronic disturbances simultaneously are important for preventing its potential damages and determining potential remedies. Existing anomaly detection methods are severely limited by the low accuracy, the expensive computational cost and the need for highly trained personnel. There is an urgent need for a scalable online algorithm for in-field analysis of large-scale power electronics networks. Here in this paper, we propose a fast and accurate algorithm for anomaly detection and localization of power electronics networks: stratified colored-node graph (CONGO2). This algorithm hierarchically models the change of correlated waveforms and then correlated sensors using the colored-node graph. By aggregating the change of each sensor with its neighbors’ inputs, we can spontaneously identify and localize the anomaly that cannot be detected by data collected from a single sensor. As our proposed method only focuses on the changes within a short time frame, it is highly computational efficient and only needs small data storage. Thus, our method is ideal for online and reliable anomaly detection and localization of large-scale power electronic networks. Compared to existing anomaly detection methods, our method is entirely data-driven without training data, highly accurate and reliable for wide-spectrum anomalies detection, and more importantly, capable of both detection and localization. Thus, it is ideal for infield deployment for large-scale power electronic networks. As illustrated by a distributed energy resources (DERs) power grid with 37-node, our method can effectively detect and localize various cyber and physical attacks.

• Research Organization: University of Arkansas, Fayetteville, AR (United States); University of Georgia, Athens, GA (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Science Foundation (NSF); National Institute of Health (NIH); US Department of Defense (DoD); National Science Foundation of China; Beijing Institute of Technology
• Grant/Contract Number: EE0009026
• OSTI ID: 1980405
• Journal Information: IEEE Internet of Things Journal (Online), Journal Name: IEEE Internet of Things Journal (Online) Journal Issue: 15 Vol. 9; ISSN 2327-4662
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English