神经建模注入EMT分析

IF 4.3 2区计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-14 DOI:10.1109/TCE.2025.3542069

Qing Shen;Yifan Zhou;Peng Zhang;Yacov A. Shamash;Roshan Sharma;Bo Chen

{"title":"神经建模注入EMT分析","authors":"Qing Shen;Yifan Zhou;Peng Zhang;Yacov A. Shamash;Roshan Sharma;Bo Chen","doi":"10.1109/TCE.2025.3542069","DOIUrl":null,"url":null,"abstract":"The paper pioneers and optimizes a systematic approach to developing Physics-Informed neuro-Models (PIM) for the transient analysis of power grids interconnected with inverter-based resources. PIM serves as an effective online digital twin of power components, incorporating physical knowledge and preserving the system’s nonlinear differential structure while requiring only minimal data for training. Three contributions are presented: 1) An Physics Informed Neural Network (PINN)-enabled neuro-modeling approach for constructing an accurate ElectroMagnetic Transient (EMT) model; 2) A data-physics hybrid, multi-neural learning structure that demonstrates PIM’s adaptability at varying levels of data availability; 3) A balanced-adaptive PIM automatically optimizes the learning process while ensuring alignment with physical principles. Tests on rotating and static electrical components, as well as an IEEE test system, validate its efficacy for transient grid analysis under diverse operational scenarios.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1806-1818"},"PeriodicalIF":4.3000,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neuro-Modeling Infused EMT Analytics\",\"authors\":\"Qing Shen;Yifan Zhou;Peng Zhang;Yacov A. Shamash;Roshan Sharma;Bo Chen\",\"doi\":\"10.1109/TCE.2025.3542069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper pioneers and optimizes a systematic approach to developing Physics-Informed neuro-Models (PIM) for the transient analysis of power grids interconnected with inverter-based resources. PIM serves as an effective online digital twin of power components, incorporating physical knowledge and preserving the system’s nonlinear differential structure while requiring only minimal data for training. Three contributions are presented: 1) An Physics Informed Neural Network (PINN)-enabled neuro-modeling approach for constructing an accurate ElectroMagnetic Transient (EMT) model; 2) A data-physics hybrid, multi-neural learning structure that demonstrates PIM’s adaptability at varying levels of data availability; 3) A balanced-adaptive PIM automatically optimizes the learning process while ensuring alignment with physical principles. Tests on rotating and static electrical components, as well as an IEEE test system, validate its efficacy for transient grid analysis under diverse operational scenarios.\",\"PeriodicalId\":13208,\"journal\":{\"name\":\"IEEE Transactions on Consumer Electronics\",\"volume\":\"71 1\",\"pages\":\"1806-1818\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-02-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Consumer Electronics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10887332/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Consumer Electronics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10887332/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

摘要

本文开创并优化了一种系统的方法来开发物理信息神经模型（PIM），用于与基于逆变器的资源互联的电网的瞬态分析。PIM作为一个有效的在线数字孪生电源组件，结合物理知识和保留系统的非线性微分结构，同时只需要最少的数据进行训练。本文提出了三个贡献：1)基于物理信息神经网络（PINN）的神经建模方法，用于构建精确的电磁瞬变（EMT）模型；2)数据物理混合、多神经学习结构，证明PIM在不同数据可用性水平下的适应性；3)平衡自适应PIM自动优化学习过程，同时确保与物理原理保持一致。通过对旋转和静态电气元件以及IEEE测试系统的测试，验证了该方法在各种运行场景下对暂态电网分析的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Neuro-Modeling Infused EMT Analytics

The paper pioneers and optimizes a systematic approach to developing Physics-Informed neuro-Models (PIM) for the transient analysis of power grids interconnected with inverter-based resources. PIM serves as an effective online digital twin of power components, incorporating physical knowledge and preserving the system’s nonlinear differential structure while requiring only minimal data for training. Three contributions are presented: 1) An Physics Informed Neural Network (PINN)-enabled neuro-modeling approach for constructing an accurate ElectroMagnetic Transient (EMT) model; 2) A data-physics hybrid, multi-neural learning structure that demonstrates PIM’s adaptability at varying levels of data availability; 3) A balanced-adaptive PIM automatically optimizes the learning process while ensuring alignment with physical principles. Tests on rotating and static electrical components, as well as an IEEE test system, validate its efficacy for transient grid analysis under diverse operational scenarios.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Transactions on Consumer Electronics 工程技术-电信学

CiteScore

7.70

自引率

9.30%

发文量

审稿时长

3.3 months

期刊介绍： The main focus for the IEEE Transactions on Consumer Electronics is the engineering and research aspects of the theory, design, construction, manufacture or end use of mass market electronics, systems, software and services for consumers.