A Physics-Informed Neural Network Modeling Approach for Energy Storage-Based Fast Frequency Support in Microgrids-Reference-Cited by-同舟云学术

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A Physics-Informed Neural Network Modeling Approach for Energy Storage-Based Fast Frequency Support in Microgrids

Published:2024-01-29 Issue: Volume:491 Page:1-5
ISSN:
Container-title:2024 IEEE Electrical Energy Storage Application and Technologies Conference (EESAT)
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Author:

Rai Astha¹,Bhujel Niranjan¹,Dhiman Vikas¹,Hummels Donald¹,Tamrakar Ujjwol²,Byrne Raymond H.²,Tonkoski Reinaldo³

Affiliation:

1. University of Maine,Orono,Maine,USA

2. Sandia National Laboratories,Albuquerque,New Mexico,USA

3. Technical University of Munich,Munich,DE

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10471192/10471196/10471220.pdf?arnumber=10471220

Reference10 articles.

1. Optimization-Based Fast-Frequency Estimation and Control of Low-Inertia Microgrids

2. Data-Driven Load Frequency Control for Stochastic Power Systems: A Deep Reinforcement Learning Method With Continuous Action Search

3. Load Frequency Control of a Multi-Area Power System: An Adaptive Fuzzy Logic Approach

4. Physics-Informed Neural Networks for Power Systems

5. Applications of Physics-Informed Neural Networks in Power Systems - A Review

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A review of control strategies for optimized microgrid operations;IET Renewable Power Generation;2024-07-19

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