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Proceedings Paper

Radial basis function networks for predicting power system harmonics
Author(s): Stephen Kaprielian; Ismail I. Jouny
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Paper Abstract

The switching operation of nonlinear electrical loads contributes greatly to harmonic distortion in power systems. Forecasting harmonic distortion levels enables power system operators to respond with the proper corrective action, thereby enhancing system manageability. Neural networks have proved to be viable alternatives in several modeling and prediction applications, including systems in which the dynamics are chaotic. In this paper, power system harmonics are predicted using Radial Basis Function networks. This approach has certain advantages over other conventional schemes, including the potential to track the harmonics in the time-varying power system environment.

Paper Details

Date Published: 22 March 1996
PDF: 7 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235910
Show Author Affiliations
Stephen Kaprielian, Lafayette College (United States)
Ismail I. Jouny, Lafayette College (United States)


Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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