Neural Network Design for Impedance Modeling of Power Electronic Systems Based on Latent Features
Author:
Affiliation:
1. Division of Electric Power and Energy Systems, KTH Royal Institute of Technology, Stockholm, Sweden
2. Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA
Funder
C3
KTH Digital Futures and the Princeton Campus-as-Laboratory Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software
Link
http://xplorestaging.ieee.org/ielx7/5962385/6104215/10021300.pdf?arnumber=10021300
Cited by 7 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. Machine Learning at the Grid Edge: Data-Driven Impedance Models for Model-Free Inverters;IEEE Transactions on Power Electronics;2024-08
2. Black-Box Impedance Identification and Modeling for Time-Domain Transient Analysis of Power Electronics-Based Energy Conversion Systems;2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH);2024-03-20
3. Impedance Profile Prediction for Grid-Connected VSCs based on Feature Extraction;2024 IEEE Applied Power Electronics Conference and Exposition (APEC);2024-02-25
4. Computational Intelligence-Based Learning Techniques to Handle Uncertainty and Large Data Sets;CIGRE Green Books;2024
5. Online Identification of Wind Farm Wide Frequency Admittance with Power Cables Using the Artificial Neural Network;2023 IEEE Energy Conversion Congress and Exposition (ECCE);2023-10-29
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