Standalone Deployment of Two-Fold Deep Neural Network in Distributed DC Microgrid—FDIA Detection and Mitigation Scheme

Abstract

In a distributed direct current (dc) microgrid system, the networked communication architecture enhances the accessibility of data but introduces the risk of cyberattacks. Accurate and comprehensive attack detection and mitigation techniques are essential to ensure its reliable operation, effective control, and exposure to hidden dangers and security implications. This article proposes a two-fold deep neural network (TFDNN)-based control architecture for detecting and mitigating the false data injection attack (FDIA) at the sensor level for a distributed dc microgrid system. TFDNN is a combination of two neural networks. The first neutral network predicts the converter's duty, and the second neural network detects the FDIA by producing the error value. The combination of two network outputs is the desired duty after eliminating the effect of an FDIA. Neural networks are trained with a wide range of data, including attack scenarios and system disturbances, to perform effectively for various FDIA and in-adverse conditions. Later the designed dc microgrid control is deployed into the microcontroller for standalone operation. The proposed scheme is implemented in real-time hardware, and the results are explored.