Physics-Informed AI Inverter

2024/06/25 by Qing Shen, Yifan Zhou, Shen, Qing +9 · 1 citation
Computer Science · #FOS: Electrical engineering #Neural Networks and Applications #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2406.17661

openalex publication_date 2024/06/25 · openalex created_date 2024/06/27 · openalex updated_date 2026/07/28

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).

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