Artificial Intelligence-based Frequency Trajectory Prediction to Enhance Power Electronics-Dominated Grid Resiliency

This paper presents an Artificial Intelligence (AI)-based frequency trajectory prediction scheme to enhance power electronics-dominated grid's (PEDG)s resiliency. The proposed approach incorporates two main modules: i) Data mining module, and ii) real-time AI-based frequency trajectory prediction module. The data mining module provides a comprehensive dataset for a wide range of system frequency and rate of change of frequency (ROCOF). This approach encompasses all practical ranges for total system inertia, vast range of load disturbances and system reactance-to-resistance (X/R) ratio. In this module, Bayesian regularization algorithm is used to compensate dataset size limitations, resolving noisy training dataset issues and cover any potential uncertainties. The AI-enabled neural network (ANN) module is incorporated in the grid following inverter's (GFLI)s control loop to adjust their pre-defined power references in real-time during grid disturbances. The functionality of the proposed approach is validated via real-time testing of a 14-bus PEDG network on an OPAL-RT platform. Obtained results attest to the theoretical methodology validation and the robustness of the proposed approach.