Dynamic Equivalent of a Power Network by Deep Artificial Neural Networks Under Varying Power System Operating Conditions-Reference-Cited by-同舟云学术

Dynamic Equivalent of a Power Network by Deep Artificial Neural Networks Under Varying Power System Operating Conditions

Published:2022-11-20 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC)
language:
Short-container-title:

Author:

Aththanayake Lahiru¹,Hosseinzadeh Nasser¹^ORCID,Gargoom Ameen¹

Affiliation:

1. School of Engineering, SEBE, Deakin University,Centre for Smart Power and Energy Research,Melbourne,Australia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10072006/10072029/10072231.pdf?arnumber=10072231

Reference34 articles.

1. Order Reduction of Linear State-Space Models Via Optimal Approximation of the Nondominant Modes

2. Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction

3. IEEE PES task force on benchmark systems for stability controls simplified 14-generator model of the south east Australian power system;gibbard;University of Adelaide,2014

4. Dynamic Aggregation of Generating Unit Models

5. Identification of Coherent Generators for Dynamic Equivalents

Cited by 2 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. An Adaptive Dynamic Model for Reducing the Size of A Power System for Large Variations in the Operating Point using LSTM RNNs;2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG);2023-12-03

2. Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis;IEEE Access;2023