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DOE OSTI · 1867210

A Hybrid Data-Driven and Model-Based Anomaly Detection Scheme for DER Operation: Preprint

Yao, Yiyun (ORCID:000000033847815X)·Ding, Fei·Liu, Weijia
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Abstract

This paper proposes a hybrid data and model-based anomaly detection for securing the operation of distributed energy resources (DERs) in distribution grids. Data-driven autoencoders (AE) are set up at the edge level by taking local DER data and detect anomalous operations by leveraging the reconstruction ability. In parallel, model-based state estimation (SE) is running at the system level by taking system models and measurements, the anomalies are identified by analyzing the measurements residual. The hybrid scheme preserves the benefits of both data-driven and model-based analysis and thus improves the robustness and accuracy of anomaly detection. It can be established by getting full use of the existing infrastructures in distribution grids. Numerical tests on a realistic distribution feeder in Southern California highlight the effectiveness as well as benefits of the proposed scheme.

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anomaly detectionautoencoderlargest normalized residualPOWER TRANSMISSION AND DISTRIBUTIONstate estimation

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Yao, Yiyun (ORCID:000000033847815X), Ding, Fei, Liu, Weijia. 2022-05-04. A Hybrid Data-Driven and Model-Based Anomaly Detection Scheme for DER Operation: Preprint. https://www.osti.gov/biblio/1867210

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