Recurrent Neural Network-Based Sensor Data Attacks Identification in Distributed Renewable Energy-Based DC Microgrid

In the control system of a microgrid, the integrity of sensor data is crucial, and the potential for attacks on this data poses a significant threat. Such attacks can lead to system instability and, in the worst case, a blackout in the serviced area. This study focuses on detecting these attacks by employing a specialized type of neural network known as a recurrent neural network (RNN). The proposed approach involves using Deep learning techniques for attack detection, utilizing time-series sensor data collected under various conditions, including load changes and normal operation. The RNN functions as a state observer, closely monitoring the output of converters and aligning with actual measurements. Through effective error estimation, the AI-based solution successfully identifies anomalies in local measurements. By integrating this error information, the attack detection system gains the capability to categorize the estimated error, indicating the presence of an attack on the system. The effectiveness of the proposed system is validated using MATLAB, and realtime validation is conducted using OPAL-RT.