Co-Predictor-Based Resilient Control of Nonlinear Load Frequency Control Systems Under FDI Attacks and Large Delays

Area of Science:

• Electrical Engineering
• Control Systems
• Cybersecurity

Background:

• Wind power systems (WPSs) face challenges from time-varying uncertainties, communication delays, and false data injection attacks (FDIAs).
• Maintaining stable grid frequency is crucial for reliable power supply, especially with increasing integration of variable renewable energy sources.
• Existing control strategies often struggle with the combined effects of large delays and sophisticated cyber threats.

Purpose of the Study:

• To develop a robust load frequency control (LFC) strategy for WPSs that addresses time-varying uncertainties, large input delays, and FDIAs.
• To enhance the frequency stability and cyber-resilience of interconnected power systems with significant wind power penetration.
• To propose a novel prediction-based control framework capable of handling complex system dynamics and external disturbances.

Main Methods:

• Utilized a Takagi-Sugeno (T-S) fuzzy model to represent the nonlinear dynamics of WPS, incorporating operational fluctuations and the impact of delays and FDIAs.
• Developed a sequential co-predictor cascade framework with a collaborative state-attack prediction mechanism to manage large delays by partitioning them into subintervals.
• Employed a unified co-design approach for fuzzy control laws and subpredictor gains to ensure closed-loop system stability, relying solely on output measurements for state and attack signal prediction.

Main Results:

• The proposed co-predictor-based LFC strategy effectively manages large input delays and predicts false data injection attacks (FDIAs) using output measurements.
• Simulations demonstrated superior performance in enhancing frequency stability compared to existing methods under various challenging scenarios.
• The strategy significantly improved the cyber-resilience of wind power systems against sophisticated cyber attacks.

Conclusions:

• The co-predictor-based LFC strategy offers a robust solution for controlling wind power systems subject to uncertainties, delays, and FDIAs.
• The hierarchical prediction framework effectively mitigates the impact of large communication delays and enhances system security.
• The proposed method represents a significant advancement in ensuring reliable and secure operation of modern power grids with high wind energy penetration.