Advance learning technique for the electricity market attack detection. (May 2022) - Huang, Yikun; He, Haolin
Highlights: Propose a real-time deep learning-based attack detection and mitigation. Proposed a general framework that can minimize more complications of the FDIAs detection. The detection approach in this case study is capable of detecting a cyberattack as well as load variations. Propose FDIA detection which is the offline digital time-domain method. Abstract: The false data injection (FDI) attacks in the AC smart microgrid (SMG) and the solution for detecting attacks on distributed energy sources in the SMG are discussed in the present paper. As the input indices for the deep machine learning (DML) algorithm, wavelet singular amounts are used as an alternative method of detecting FDI attacks (FDIAs). By means of the suggested approach, a specified error is used to adjust the precision factors of the wavelet transform and subsequently, singular value decomposition is used to retrieve features from wavelet coefficients. The wavelet singular values in switching surfaces of current and voltage are used to determine the indexes used for DML and diagnosing FDIA. Detecting FDIA with great precision is the main goal of the suggested detection plan. A simulation study using MATLAB/Simulink is conducted on SMG to demonstrate the performance of the suggested approach. Graphical abstarct: Microgrid Cyber-physical structure. Image, graphical abstract