Abstract

The integration of Permanent Magnet Synchronous Generator (PMSG) 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 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 various
cyber threats, such as False Data Injection Attacks (FDIAs). These attacks can compromise power
grids’ stability and operational integrity and result in blackouts. On this basis, we highlight the
vulnerability of communication links of PMSG-based WF 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 wind farm and WT controllers. Second, the CNN model is trained
using grid and wind farm 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 wind farm connected to a power system and compared with traditional data-driven
detection methods based on K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient
Boosting (XGBoost). The results demonstrate that CNN achieves high detection rates with
minimal false positives, validating its efficiency in detecting FDIAs in grid-connected wind farms.