By Topic

Real-time transient stability assessment model using extreme learning machine

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Xu, Y. ; Dept. of Electr. Eng., Hong Kong Polytech. Univ., Kowloon, China ; Dong, Z.Y. ; Meng, K. ; Zhang, R.
more authors

In recent years, computational intelligence and machine learning techniques have gained popularity to facilitate very fast dynamic security assessment for earlier detection of the risk of blackouts. However, many of the current state-of-the-art models usually suffer from excessive training time and complex parameters tuning problems, leading to inefficiency for real-time implementation and on-line model updating. In this study, a new transient stability assessment model using the increasingly prevalent extreme learning machine theory is developed. It has significantly improved the learning speed and can enable effective on-line updating. The proposed model is examined on the New England 39-bus test system, and compared with some state-of-the-art methods in terms of computation time and prediction accuracy. The simulation results show that the proposed model possesses significant superior computation speed and competitively high accuracy.

Published in:

Generation, Transmission & Distribution, IET  (Volume:5 ,  Issue: 3 )