NDLI: Orthogonal least squares learning algorithm based radial basis function (RBF) network adaptive power system stabilizer

Content Provider	IEEE Xplore Digital Library
Author	Kothari, M.L. Madnani, S. Segal, R.
Copyright Year	1997
Description	Author affiliation: Dept. of Electr. Eng., Indian Inst. of Technol., New Delhi, India (Kothari, M.L.)
Abstract	The paper presents a systematic approach for designing a radial basis function (RBF) network based adaptive power system stabilizer using orthogonal least squares learning algorithm. The training patterns are generated over a wide range in machine real/reactive power output and terminal voltage using linearized model of the system. Investigations reveal that the required number of RBF centers heavily depend on the spread factor /spl beta/ and tolerance expressed as sum of squared errors. Studies reveal that the dynamic performance of the system with radial basis function network adaptive power system stabilizer (RBFAPSS) is superior to that with a conventional PSS. Moreover, RBFAPSS provides optimum performance for a wide range in loading conditions and large perturbations.
Starting Page	542
Ending Page	547
File Size	457513
Page Count	6
File Format	PDF
ISBN	0780340531
ISSN	1062922X
DOI	10.1109/ICSMC.1997.625808
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least squares methods Adaptive systems Radial basis function networks Artificial neural networks Power system stability Power system modeling Power system dynamics Backpropagation algorithms Algorithm design and analysis Damping
Content Type	Text
Resource Type	Article

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