Physics-Informed AI Inverter
Fuente:
arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911951943106560 |
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| author | Shen, Qing Zhou, Yifan Zhang, Peng Shamash, Yacov A. Sharma, Roshan Chen, Bo |
| author_facet | Shen, Qing Zhou, Yifan Zhang, Peng Shamash, Yacov A. Sharma, Roshan Chen, Bo |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17661 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics-Informed AI Inverter Shen, Qing Zhou, Yifan Zhang, Peng Shamash, Yacov A. Sharma, Roshan Chen, Bo Systems and Control 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). |
| title | Physics-Informed AI Inverter |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2406.17661 |