Physics-Informed AI Inverter

TL;DR

This paper introduces a physics-informed neural network-based AI inverter for electromagnetic transient simulations, demonstrating improved accuracy and efficiency over traditional methods through extensive validation.

Contribution

It presents a novel PINN-enabled AI inverter, a balanced-adaptive training strategy, and comprehensive validation showing its advantages over classical EMT programs.

Findings

AI-Inverter achieves higher accuracy than traditional EMT methods.

The balanced-adaptive PINN improves training efficiency.

Extensive validation confirms the AI-Inverter's superiority.

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).