A Novel ConvXGBoost Method for Detection and Identification of Cyberattacks on Grid-Connected Photovoltaic (PV) Inverter System

The integration of solar Photovoltaic (PV) systems into the AC grid poses stability challenges, especially with increasing inverter-based resources. For an efficient operation of the system, smart grid-forming inverters need to communicate with the Supervisory Control and Data Acquisition (SCADA) system. However, Internet-of-Things devices that communicate with SCADA make these systems vulnerable. Though many researchers proposed Artificial-Intelligence-based detection strategies, identification of the location of the attack is not considered by these strategies. To overcome this drawback, this paper proposes a novel Convolution extreme gradient boosting (ConvXGBoost) method for not only detecting Denial of Service (DoS) and False Data Injection (FDI) attacks but also identifying the location and component of the system that was compromised. The proposed model is compared with the existing Convolution Neural Network (CNN) and decision tree (DT) strategies. Simulation results demonstrate the effectiveness of the proposed method for both the smart PV and PV fuel cell (PV-FC) systems. For example, the proposed model is efficient with an accuracy of 99.25% compared to the 97.76% of CNN and 99.12% of DT during a DoS attack on a smart PV system. Moreover, the proposed method can detect and identify the attack location faster than other models.