Digital twin application for attach detection and mitigation of PV-based smart systems using fast and accurate hybrid machine learning algorithm

Microgrids (MG) is originally designed to make Smart Grids more energy-efficient and reliable. The MG repre-sents a complex cyber-physical system whose functioning is driven by the interaction of physical actions and computational elements, making it vulnerable to many forms of malicious cyber-attack. This study considers the effect of data integrity attack (DIA) on the performance of hybrid MGs, an important cyber threat to microgrids (MGs). Further, this paper develops a new process using sequential hypothesis testing (SHT) for detecting DIA on renewable energy resources and improving the security of information in hybrid MGs. By using a binary sample generated from the suggested approach, an analysis statistic is computed and afterward compared to 2 thresholds for deciding between the 3 options. On the measured energy production of renewable energy sources such as wind turbines, DIAs of various severity levels have been conducted for assessing the impact of DIAs on hybrid MG security. A standard IEEE test system has been used to evaluate the efficiency of the suggested process.

Automated summary

This study examines the impact of data integrity attacks on hybrid microgrids and proposes a new process using sequential hypothesis testing to detect these attacks on renewable energy resources and enhance the security of information in hybrid microgrids.