scholarly journals Erratum to: Real-time transient stability status prediction using cost-sensitive extreme learning machine

2015 ◽  
Vol 27 (2) ◽  
pp. 333-333
Author(s):  
Zhen Chen ◽  
Xianyong Xiao ◽  
Changsong Li ◽  
Yin Zhang ◽  
Qingquan Hu
Keyword(s):  
Real Time ◽  
Extreme Learning Machine ◽  
Transient Stability ◽  
Learning Machine

Real-Time Transient Stability Assessment Based on Genetic Algorithm-Extreme Learning Machine

2013 ◽  
Vol 427-429 ◽  
pp. 1390-1393
Author(s):  
Bo Wang ◽  
Ke Wang ◽  
Da Hai You ◽  
Wei Hua Chen ◽  
Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
New England ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Transient Stability ◽  
Assessment Method ◽  
Stability Assessment ◽  
Electromagnetic Power ◽  
Hidden Biases ◽  
Learning Machine

In this paper an genetic algorithm-extreme learning machine (ELM) based real-time transient stability assessment method is proposed. This method uses genetic algorithm (GA) to search optimal input weights and hidden biases in the principle of cross validation to establish GA-ELM classifier. In order to do real-time transient stability assessment, generator trajectories of rotor angle, rotor speed, voltage magnitude, electromagnetic power and imbalance power in-and post-disturbance are chosen as original features for the quick access based synchronously sampled values. Simulation results of New-England 39-bus system show that this method has good performance in power system transient stability assessment.

Real-time transient stability status prediction using cost-sensitive extreme learning machine

2015 ◽  
Vol 27 (2) ◽  
pp. 321-331 ◽  
Author(s):  
Zhen Chen ◽  
Xianyong Xiao ◽  
Changsong Li ◽  
Yin Zhang ◽  
Qingquan Hu
Keyword(s):  
Real Time ◽  
Extreme Learning Machine ◽  
Transient Stability ◽  
Learning Machine
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