Hybrid Machine Learning Approach for Cyberattack Mitigation of Parallel Converters in a DC Microgrid

TL;DR

This paper presents a hybrid machine learning method to detect and mitigate false data injection cyberattacks on parallel DC-DC converters in DC microgrids, enhancing system security and stability.

Contribution

It introduces a novel hybrid machine learning strategy specifically designed for detecting and countering cyberattacks on parallel converters in microgrids, which is a new approach in this context.

Findings

Successfully detects FDI attacks in simulations

Prevents cyberattack-induced instability

Ensures safe operation of the microgrid

Abstract

Cyberattack susceptibilities are introduced as the communication requirement increases with the incorporation of more renewable energy sources into DC microgrids. Parallel DC-DC converters are utilized to provide high current and supply the load. Nevertheless, these systems are susceptible to cyberattacks that have the potential to disrupt operations and jeopardize stability. Voltage instability may result from the manipulation of communication commands and low-layer control signals. Therefore, in this paper, a cyberattack that specifically targets parallel DC-DC converters is examined in a DC microgrid. A hybrid machine learning-based detection and mitigation strategy is suggested as a means to counteract this threat. The false data injection (FDI) attack targeting the converters is investigated within a DC microgrid. The efficacy of the suggested approach is verified via simulations executed for various scenarios within the MATLAB/Simulink environment. The technique successfully identifies and blocks FDI attacks, preventing cyberattacks and ensuring the safe operation of the DC microgrid.