Critical Clearing Time prediction within various loads for transient stability assessment by means of the Extreme Learning Machine method

2016 ◽  
Vol 77 ◽  
pp. 345-352 ◽  
Author(s):  
Irrine Budi Sulistiawati ◽  
Ardyono Priyadi ◽  
Ony Asrarul Qudsi ◽  
Adi Soeprijanto ◽  
Naoto Yorino
Keyword(s):  
Extreme Learning Machine ◽  
Transient Stability ◽  
Stability Assessment ◽  
Machine Method ◽  
Time Prediction ◽  
Clearing Time ◽  
Learning Machine

Application of Extreme Learning Machine in Transient Stability Assessment of Power Systems

2013 ◽  
Vol 392 ◽  
pp. 544-547 ◽  
Author(s):  
Yang Li ◽  
Xue Ping Gu
Keyword(s):  
Power Systems ◽  
Extreme Learning Machine ◽  
Rough Sets ◽  
Transient Stability ◽  
Test System ◽  
System Level ◽  
Stability Assessment ◽  
Fuzzy Rough Sets ◽  
Operation Condition ◽  
Learning Machine

This paper presents a new method for transient stability assessment (TSA) of power systems using kernel fuzzy rough sets and extreme learning machine (ELM). Considering the possible real-time information provided by phasor measurement units, a group of system-level classification features were firstly extracted from the power system operation condition to construct the original feature set. Then kernelized fuzzy rough sets were used to reduce the dimension of input space, and ELM was employed to build a TSA model. The effectiveness of the proposed method is validated by the simulation results on the New England 39-bus test system.

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.

scholarly journals Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

2021 ◽  
Vol 13 (7) ◽  
pp. 3744
Author(s):  
Mingcheng Zhu ◽  
Shouqian Li ◽  
Xianglong Wei ◽  
Peng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Good Prediction ◽  
Foundation Settlement ◽  
Machine Method ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Simulation Results ◽  
Learning Machine ◽  
The Stability

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method

2018 ◽  
Vol 281 ◽  
pp. 209-221 ◽  
Author(s):  
Irfan Bahiuddin ◽  
Saiful A. Mazlan ◽  
Mohd. I. Shapiai ◽  
Seung-Bok Choi ◽  
Fitrian Imaduddin ◽  
...  
Keyword(s):  
Constitutive Model ◽  
Extreme Learning Machine ◽  
Machine Method ◽  
Learning Machine ◽  
Magneto Rheological
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