Work

Physics-Informed AI Inverter

Year: 2024

Type: preprint

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).

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2406.17661

Authors: Shen, Qing, Zhou, Yifan, Zhang, Peng, Shamash, Yacov A., Sharma, Roshan, Chen, Bo

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W4400065193

Cites:

Cited by:

Related to: 10