An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-based Systems

Inverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using meet objectives. anomalies targeting network may distort normal operation, therefore, effective anomaly mitigation technique Artificial Neural Network (ANN) is proposed in this paper. intelligent modeled a dynamic neural employs nonlinear autoregressive with exogenous inputs. effects false data injection on controller are considered. training for designing generated by multiple simulations designed microgrid under various operating conditions MATLAB/Simulink. An explainable framework employed interpret output trained network-based after has been offline and validated online simulated microgrid. applied as voltage frequency control-based regulate conditions. performance verified injecting types injection-based ANN-based maintained operation demonstrated real-time digital simulator.