Paper Info
Title
Online Levenberg-Marquardt algorithm for neural network based estimation and control of power systems
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).
Year
DOI
Venue
2009
10.1109/IJCNN.2009.5179071
IJCNN
Keywords
Field
DocType
test system,non-linear self-tuning control,online lm method,successive disturbance use,nonlinear estimation,non-linear neural network structure,neural network,rls estimation,online estimation,online levenberg-marquardt algorithm,power system dynamic behavior,neural networks,oscillators,estimation,lms algorithm,convergence,thyristors,learning artificial intelligence,adaptive control,artificial neural networks,power system,feedback linearization,neurofeedback,damping,data mining,control systems,power systems,feedback,levenberg marquardt
Convergence (routing),Nonlinear system,Computer science,Control theory,Feedback linearization,Electric power system,Control system,Adaptive control,Artificial neural network,Levenberg–Marquardt algorithm
Conference
ISSN
Citations 
PageRank 
2161-4393
2
0.42
References 
Authors
3
4
Name
Order
Citations
PageRank
Jawad Arif120.42
Nilanjan Ray Chaudhuri2155.52
Swakshar Ray3112.19
B. Chaudhuri411116.05