Observer-Based Fuzzy Event-Triggered LFC for Wind Power Systems Under Multiple Cyberattacks via an NN Approximation Approach

This paper presents an observer-based fuzzy event-triggered load frequency control (LFC) strategy and a neural network (NN) approximation approach for wind power systems (WPSs) facing network resource constraints and multiple cyberattacks. First, a Takagi-Sugeno (T-S) fuzzy model is developed to capture the nonlinear dynamics of the wind turbine. A novel multiple cyberattack model is then proposed, relaxing stringent assumptions about denial-of-service (DoS) and false data injection (FDI) attacks prevalent in prior studies. An observer-based dynamic event-triggered controller is subsequently designed to reduce network burden in WPSs while mitigating DoS effects. This controller incorporates an NN-based online weight adjustment algorithm to approximate and counteract the false data injected by FDI attacks. Lyapunov analysis demonstrates that the augmented error system achieves asymptotic stability with H<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance in the absence of DoS attacks and maintains uniformly ultimate boundedness under multiple cyberattacks. Finally, a numerical example validates the effectiveness of the proposed control strategy and NN approximation approach.