Empirical Study of Model Repair of DNN for Power System Transient Stability Assessment-Reference-Cited by-同舟云学术

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Empirical Study of Model Repair of DNN for Power System Transient Stability Assessment

Published:2023-12-15 Issue: Volume:43 Page:1-5
ISSN:
Container-title:2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2)
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Author:

Liu Siyuan¹,Zhou Xiaotian¹,Zheng Le¹,Ma Huan²,Ma Linlin³,Wang Ting³

Affiliation:

1. School of Electrical and Electronic Engineering, North China Electric Power University,Beijing,China

2. Power Grid Technology Center State Grid, Shandong Electric Power Research Institute,Jinan,China

3. Shandong Electric Power Dispatching and Control Center State Grid, Shandong Electric Power Company,Jinan,China

Funder

National Key Research and Development Program of China

SGCC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10511272/10512350/10512608.pdf?arnumber=10512608

Reference15 articles.

1. Deep belief network based nonlinear representation learning for transient stability assessment

2. Real-time transient stability assessment based on deep recurrent neural network

3. A data-driven method for online transient stability monitoring with vision-transformer networks

4. Multi-stage transient stability assessment of power system based on self-attention transformer encoder;Fang,2023

5. Hierarchical Deep Learning Machine for Power System Online Transient Stability Prediction

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