Physics-Informed AI Inverter
<|reference_start|>Physics-Informed AI Inverter: 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).<|reference_end|>
@article{shen2024physics-informed,
    title={Physics-Informed AI Inverter},
    author={Qing Shen, Yifan Zhou, Peng Zhang, Yacov A. Shamash, Roshan Sharma, Bo
  Chen},
    journal={arXiv preprint arXiv:2406.17661},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17661},
    primaryClass={eess.SY cs.SY}
}