A dagging‐based deep learning framework for transmission line flexibility assessment-Reference-Cited by-同舟云学术

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A dagging‐based deep learning framework for transmission line flexibility assessment

Published:2022-12-17 Issue:5 Volume:17 Page:1092-1105
ISSN:1752-1416
Container-title:IET Renewable Power Generation
language:en
Short-container-title:IET Renewable Power Gen

Author:

Morteza Azita¹,Sadipour Masod²,Fard Reza Saadati³,Taheri Saman¹^ORCID,Ahmadi Amirhossein⁴^ORCID

Affiliation:

1. Department of Civil, Environmental, and Ocean Engineering Steven Institute of Technology Hoboken New Jersey USA

2. Department of Mechanical Engineering University of Denver Colorado USA

3. Department of Computer Science Worcester Polytechnic Institute Worcester USA

4. Department of Electrical Engineering, Tehran Polytechnic Amirkabir University of Technology Tehran Iran

Publisher

Institution of Engineering and Technology (IET)

Subject

Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/rpg2.12663

Reference55 articles.

1. Experimentally validated partial least squares model for dynamic line rating

2. Coordinated investment in wind‐rich regions using dynamic line rating, energy storage and distributed static series compensation to facilitate congestion management

3. Enlarging flexibility region of virtual power plant via dynamic line rating

4. Optimal scheduling of reconfigurable grids considering dynamic line rating constraint;Mohamed M.A.;IET Renew. Power Gener.,2020

5. A New False Data Injection Attack Detection Model for Cyberattack Resilient Energy Forecasting

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