A Novel Data-Driven LSTM-SAF Model for Power Systems Transient Stability Assessment-Reference-Cited by-同舟云学术

A Novel Data-Driven LSTM-SAF Model for Power Systems Transient Stability Assessment

Published:2024-07 Issue:7 Volume:20 Page:9083-9097
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Shao Zonghe¹^ORCID,Wang Qichao¹^ORCID,Cao Yuzhe¹^ORCID,Cai Defu²^ORCID,You Yang¹^ORCID,Lu Renzhi³^ORCID

Affiliation:

1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

2. State Grid Hubei Electric Power Research Institute, Wuhan, China

3. Engineering Research Center of Autonomous Intelligent Unmanned Systems, Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

Funder

Science and Technology Project of State Grid Corporation of China, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10582275/10495722.pdf?arnumber=10495722

Reference47 articles.

1. An Intelligent Transient Stability Assessment Framework With Continual Learning Ability

2. Individual Functions Method for Power System Transient Stability Assessment

3. Modeling and Transient Stability Analysis for Type-3 Wind Turbines Using Singular Perturbation and Lyapunov Methods

4. A Simulation-Based Classification Approach for Online Prediction of Generator Dynamic Behavior Under Multiple Large Disturbances

5. Probabilistic Framework for Assessing the Accuracy of Data Mining Tool for Online Prediction of Transient Stability

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