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