Online Levenberg-Marquardt algorithm for neural network based estimation and control of power systems

Jawad Arif, Nilanjan Ray Chaudhuri, Swakshar Ray, Balarko Chaudhuri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

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).

Original languageEnglish (US)
Title of host publication2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages199-206
Number of pages8
DOIs
StatePublished - Nov 18 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: Jun 14 2009Jun 19 2009

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2009 International Joint Conference on Neural Networks, IJCNN 2009
CountryUnited States
CityAtlanta, GA
Period6/14/096/19/09

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All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Arif, J., Chaudhuri, N. R., Ray, S., & Chaudhuri, B. (2009). Online Levenberg-Marquardt algorithm for neural network based estimation and control of power systems. In 2009 International Joint Conference on Neural Networks, IJCNN 2009 (pp. 199-206). [5179071] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2009.5179071