Hierarchical Deep Learning Machine for Power System Online Transient Stability Prediction

2020 ◽  
Vol 35 (3) ◽  
pp. 2399-2411 ◽  
Author(s):  
Lipeng Zhu ◽  
David J. Hill ◽  
Chao Lu
Keyword(s):  
Deep Learning ◽  
Power System ◽  
Transient Stability ◽  
Stability Prediction ◽  
Learning Machine

scholarly journals Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yanjun Zhang ◽  
Tie Li ◽  
Guangyu Na ◽  
Guoqing Li ◽  
Yang Li
Keyword(s):  
Power Systems ◽  
Power System ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Transient Stability ◽  
Stability Prediction ◽  
Learning Machine ◽  
Elm Classifier ◽  
Ipso Algorithm ◽  
Synchronized Measurements

A new optimized extreme learning machine- (ELM-) based method for power system transient stability prediction (TSP) using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO) algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.

Power System Transient Stability Control Method Based on Deep Learning Hybrid Model

2021 ◽  
Author(s):  
Xin Zhang ◽  
Yawen Wang ◽  
Penghai Xie ◽  
Sajia Lin ◽  
Hanxing Luo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power System ◽  
Hybrid Model ◽  
Transient Stability ◽  
Control Method ◽  
Stability Control
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