Physics-Informed AI Inverter

Abstract

This letter devises an AI-Inverter that pilots the use of a physics-informed neural network (PINN) to enable AI-based electromagnetic transient simulations (EMT) of grid-forming inverters. The contributions are threefold: (1) A PINN-enabled AI-Inverter is formulated; (2) An enhanced learning strategy, balanced-adaptive PINN, is devised; (3) extensive validations and comparative analysis of the accuracy and efficiency of AI-Inverter are made to show its superiority over the classical electromagnetic transient programs (EMTP).

We are working on significantly expanding the research(methodology and test cases), and the current version does not accurately reflect our findings. Need more experiments to draw the conclusion. The experiments are still undergoing. We need more time to refine it. It is not ready to be public