Cyber-Attack Detection in DC Microgrids Based on Deep Machine Learning and Wavelet Singular Values Approach

Abstract

Nowadays, the role of cyber-physical systems (CPSs) is of paramount importance in power system security since they are more vulnerable to different cyber-attacks. Detection of cyber-attacks on a direct current microgrid (DC-MG) has become a pivotal issue due to the increasing use of them in various electrical engineering applications, from renewable power generations to the distribution of electricity and power system of public transportation and subway electric network. In this study, a novel strategy was provided to diagnose possible false data injection attacks (FDIA) in DC-MGs to enhance the cyber-security of electrical systems. Accordingly, to diagnose cyber-attacks in DC-MG and to identify the FDIA to distributed energy resource (DER) unit, a new procedure of wavelet transform (WT) and singular value decomposition (SVD) based on deep machine learning was proposed. Additionally, this paper presents a developed selective ensemble deep learning (DL) approach using the gray wolf optimization (GWO) algorithm to identify the FDIA in DC-MG. In the first stage, in the paper, to gather sufficient data within the ordinary performance required for the training of the DL network, a DC-MG was operated and controlled with no FDIAs. In the information generation procedure, load changing was considered to have diagnosing datasets for cyber-attack and load variation schemes. The obtained simulation results were compared with the new Shallow model and Hilbert Huang Transform methods, and the results confirmed that the presented approach could more precisely and robustly identify multiple forms of FDIAs with more than 95% precision.