Event-Triggered Model-Free Adaptive Predictive Control for Networked Wind-Power Microgrids Subject to Aperiodic DoS Attacks

Escalating cybersecurity risks in communication networks pose severe challenges to the stability and reliability of microgrid control systems. This paper investigates the load frequency control problem of nonlinear networked wind-power microgrids under denial-of-service (DoS) attacks and event-triggered communication. A data-driven prediction framework is first established to construct a control-oriented dynamic linearization data model for the underlying nonlinear system. Sub-sequently, a novel event-triggered model-free adaptive predictive control (ET-MFAPC) algorithm with time-varying parameters is proposed, which integrates multi-step adaptive prediction and receding-horizon optimization. The proposed algorithm features several key innovations: 1) An output-related auxiliary variable is designed to circumvent unavailable pseudo-partial derivatives (PPDs) and their sign constraints. 2) An adaptive predictive compensation module is developed to mitigate the impacts of DoS attacks by reconstructing hijacked data packets. 3) An attack-aware online optimization mechanism is formulated to adaptively tune controller gains for improved system performance. Furthermore, a comprehensive security analysis is conducted based on a model-independent data energy function, and explicit boundary conditions are derived for tolerable attack frequency and duration. Finally, the effectiveness and robustness of the proposed method are validated through simulation studies.