Early warning of low-frequency oscillations in power system with Vinnicombe criterion fused with PMU data
Author:
Affiliation:
1. Beijing University of Civil Engineering and Architecture,School of Mechatronics and Vehicle Engineering,Beijing,China
Funder
Beijing University of Civil Engineering and Architecture
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10353070/10353550/10353783.pdf?arnumber=10353783
Reference15 articles.
1. Intelligent Early Warning of Power System Dynamic Insecurity Risk: Toward Optimal Accuracy-Earliness Tradeoff
2. Small-signal Stability Assessment of Power System Based on Convolutional Neural Network;Li;Automation of Electric Power Systems,2019
3. A new online realization method of locating low frequency oscillation source in power grid based on PMU
4. Artificial Intelligence-Based Approach for Forced Oscillation Source Detection and Classification
5. Application of Two-stage Random Forest Classification Method to Low-frequency Oscillation Monitoring;Zhao;Journal of Northeast Electric Power University,2020
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