Load Margin Assessment of Power Systems using Recurrent Neural Network and Greylag Goose Optimization-Reference-Cited by-同舟云学术

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Load Margin Assessment of Power Systems using Recurrent Neural Network and Greylag Goose Optimization

Published:2024 Issue:13 Volume:58 Page:656-661
ISSN:2405-8963
Container-title:IFAC-PapersOnLine
language:en
Short-container-title:IFAC-PapersOnLine

Author:

Bento Murilo E.C.

Publisher

Elsevier BV

Reference55 articles.

1. Real-time control of a battery energy storage system using a reconfigurable synchrophasor-based control system;Adhikari;Energies,2023

2. A critical review of state-of-the-art optimal pmu placement techniques;Ahmed;Energies,2022

3. The continuation power flow: a tool for steady state voltage stability analysis;Ajjarapu;IEEE Transactions on Power Systems,1992

4. Ajjarapu, V. (2007). Computational Techniques for Voltage Stability Assessment and Control. Springer US. doi:10.1007/978-0-387-32935-2.

5. Bento, M.E.C. (2016a). Design analysis of wide-area damping controllers using genetic algorithms. In 2016 12th IEEE International Conference on Industry Applications (INDUSCON). doi:10.1109/induscon.2016.7874508.

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