Research Information System
2025 IEEE International Conference on Prognostics and Health Management, ICPHM 2025, Colorado, United States Of America, 9 - 11 June 2025, (Full Text)
The increasing integration of Wind Turbines (WTs) into the power grid requires advanced control strategies to maintain stability and damp oscillations, particularly under weak grid conditions. These strategies along with Wind Farm (WF) control loops often rely on data transfer and information provided by communication networks. However, the cyber layers used for such data transfer make the entire network prone to cyber threats, such as False Data Injection Attacks (FDIAs). These attacks can compromise the stability and operational integrity of power grids and result in blackouts. On this basis, in this paper, we highlight the vulnerability of WF communication points to FDIAs and propose a data-driven detection system developed based on a Convolutional Neural Network (CNN) to identify threats. First, FDIAs are introduced in the simulation by manipulating the communicated signals between the SCADA system of the WF and WT controllers. Second, the CNN model is trained using grid and WF data in various operating conditions to detect FDIAs, distinguishing them from normal operational variations. To evaluate the performance of the proposed detection method, it is tested in a WF connected to a power system and compared with traditional data-driven detection methods based on K-Nearest Neighbors (KNN), Random Forest, and XGBoost. The results demonstrate that CNN achieves high detection rates with minimal false positives, validating its efficiency in detecting FDIAs in grid-connected WFs.