Detection and Localization of Stealth False Data Injection Attacks in Active Power Distribution Systems Using an Ensemble of Deep CNNs

Cyber-physical power systems” reliance on cyberspace makes them vulnerable to cyber-attacks, particularly false data injection attacks (FDIAs), where the aim is to alter the state estimation (SE) results by changing meters” readings. Because of distribution systems” properties such as the lower measurement redundancy and varying loads, existing methods cannot be used to accurately detect and localize an FDIA. To fill these gaps and to deal with the rarity of FDIAs in distribution systems, we propose an ensemble of deep convolutional neural networks (CNNs) to detect and localize FDIAs in active balance, and unbalanced distribution systems. To this end, first, a dataset is created using different attacking scenarios and the possible reconfigurations and renewable generation scenarios in the system. The records are in the form of WLS-generated voltage estimation of PQ buses with different balancing ratios between attacked and normal records. Then, these datasets are used to train different CNNs. These CNNs' outputs are merged using a multilayer perceptron network. Finally, FDIA is detected and localized by the proposed ensemble model in a balanced and unbalanced power distribution system. Results of simulations on IEEE 33-bus and the modified IEEE 13-bus networks verify that the ensemble model can distinguish between normal and attacked records with great accuracy according to the area under the curve (AUC) criteria, thus giving the operators a powerful tool to defend against FDIAs.