Multivariate time-series cyberattack detection in the distributed secondary control of AC microgrids with convolutional neural network autoencoder ensemble - Roshanzadeh, Behshad; Choi, Jeewon; Bidram, Ali; Martínez-Ramón, Manel (Sustainable Energy, Grids and Networks, 2024)
This paper proposes an unsupervised machine learning-based approach for cyberattack detection in AC microgrids with distributed secondary control architecture. The proposed approach is fully unsupervised and only utilizes the system’s normal datasets for the training of the algorithm. The attack under study is a false data injection (FDI) attack tampering with the operating frequency of inverter-based distributed generators (DGs). The paper utilizes a 1D Convolutional Autoencoder (CAE) for cyberattack detection on a microgrid’s distributed secondary frequency control. An autoencoder is a neural network architecture, where the model is trained to reconstruct its input in an unsupervised manner. CAE can be applied to a time-series dataset to extract features and exploit the known correlation between neighboring temporal features. Due to the correlation between the operating frequency of DGs and their active power ratios, the paper uses the time series of these two variables as inputs to CAE. The effectiveness of the proposed approach has been verified using a simulated microgrid test system in Matlab/Simulink.