Online Levenberg-Marquardt algorithm for neural network based estimation and control of power systems
File(s)
Accepted version
Author(s)
Arif, J
Chaudhuri, NR
Ray, S
Chaudhuri, B
Type
Conference Paper
Abstract
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).
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).
Date Issued
2009-06-19
Date Acceptance
2009-06-14
Citation
2009, pp.199-206
ISBN
978-1-4244-3548-7
ISSN
1098-7576
Publisher
IEEE
Start Page
199
End Page
206
Copyright Statement
© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
International Joint Conference on Neural Networks
Start Date
2009-06-14
Finish Date
2009-06-19
Coverage Spatial
Atlanta, Georgia USA