Enhancing Cybersecurity in Wind Turbines Against Sensor Attacks Through Adaptive Kalman Filtering

As modern power systems increasingly integrate wind energy, the cybersecurity of wind turbine control systems becomes a critical concern. In particular, sensor attacks can compromise operational safety and lead to significant performance degradation. This paper introduces an innovative approach aimed at enhancing cybersecurity of wind turbines against such attacks. The proposed approach leverages an adaptive Kalman filter, augmented with a forgetting factor to dynamically adjust to changing conditions. The mathematical analysis of the adaptive Kalman filter's exponential stability is rigorously examined, under standard observability and controllability conditions. Through numerical simulations in two different scenarios, the effectiveness of the proposed approach is thoroughly evaluated. The results show that the adaptive Kalman filter achieves notable precision in estimating the actual magnitude of the attacks, even in the presence of noise. This resilience demonstrates the robustness of the proposed methodology in safeguarding wind turbine cybersecurity in dynamic and challenging environments.