UKF based cyber resilient consensus driven fully distributed model predictive control framework for autonomous AC microgrids

Ali, Saima

doi:10.1016/j.segan.2026.102340

UKF based cyber resilient consensus driven fully distributed model predictive control framework for autonomous AC microgrids

2026-09-01

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DOI：10.1016/j.segan.2026.102340

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This research proposes an advanced consensus-based Fully Distributed Model Predictive Control (DMPC) strat egy that is integrated with a residual-based Unscented Kalman Filter (UKF) observer for anomaly detection and mitigation, significantly enhancing cyber resilience. The proposed DMPC control enables each DG to calculate its control action autonomously based on communicated real-time state estimates through a consensus-based algo rithm. The methodology restores voltage and frequency via distributed secondary control while simultaneously optimizing active power distribution and minimizing generation costs through the integrated tertiary control strategies. UKF, as an observer for cyber-attack detection in AC microgrids (MGs), to the best of our knowledge, has not been extensively investigated to address vulnerabilities at the tertiary control layer. The UKF's capability to handle complex nonlinear dynamics and quickly identify cyber threats and anomalies has been leveraged. Accurate state estimation by the observer significantly enhances trust in operational decisions and results in greater reliability and resilience of MGs. The simulation results are obtained using MATLAB/Simulink to validate the effectiveness of the proposed control strategy under a variety of conditions.

Keywords:

AC microgrid

Distributed model predictive control

Kalman filter observer

Distributed generator

Secondary control

Tertiary control

UKF based cyber resilient consensus driven fully distributed model predictive control framework for autonomous AC microgrids

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