Referenced Items are not available for this document.
Levenberg-Marquardt (LM) algorithm, a powerful off-line batch training method for neural networks, is adapted here for online estimation of power system dynamic behavior. A special form of neural network compatible with the feedback linearization framework is used to enable non-linear self-tuning control. Use of LM is shown to yield better closed-loop performance compared to conventional recursive least square (RLS) approach. For successive disturbance use of LM in conjunction with non-linear neural network structure yields faster convergence compared to RLS. A case study on a test system demonstrates the effectiveness of the online LM method for both linear and nonlinear estimation over RLS estimation (linear).
Published in:
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Date of Conference: 14-19 June 2009
- Page(s):
- 199 - 206
- ISSN :
- 1098-7576
- E-ISBN :
- 978-1-4244-3553-1
- Print ISBN:
- 978-1-4244-3548-7
- INSPEC Accession Number:
- 10802769
- Conference Location :
- Atlanta, GA
- Digital Object Identifier :
- 10.1109/IJCNN.2009.5179071
- Product Type:
- Conference Publications
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
