Year: 2023
Type: article
Abstract: Microgrids (MGs) have gained significant attention considering their enhanced capability to integrate increasing distributed energy resources (DERs). The application of grid forming (GFM) inverters in a MG can control voltage/frequency, enable both islanded and grid-connected operation, and achieve 100% penetration. However, cyberphysical anomaly detection for a MG with GFM inverters has not been investigated before. In this paper, the cyberphysical security of an ac MG with multiple GFM inverters is comprehensively assessed by considering short-circuit high-impedance faults (HIFs) as well as firstly exploiting False Data Injection Attacks (FDIAs) against centralized communication networks. Although the applied IEEE 1547-2018 based protection function could detect abnormal conditions, there exist cyberphysical anomalies could bypass it. In order to accomplish the detection and classification of such anomaly cases, a novel LSTM-based approach is proposed to identify the multi-class pattern regarding normal, cyberphysical threats during islanded and grid-connected by utilizing time series point of common coupling (PCC) frequency data as the paramount feature to effectively reflect the system operation status. The simulation is conducted in OPAL-RT real-time environment and the effectiveness of the proposed strategy is verified with an average detection accuracy of 94.72%.
Authors: Xiaorui Liu, Hui Li
Institution: Florida State University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,950
OpenAlex ID: W4382723473