Radial basis function networks for predicting power system harmonics

Stephen Kaprielian; Ismail I. Jouny

doi:10.1117/12.235910

22 March 1996 Radial basis function networks for predicting power system harmonics

Stephen Kaprielian, Ismail I. Jouny

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235910
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

Stephen Kaprielian and Ismail I. Jouny "Radial basis function networks for predicting power system harmonics", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235910

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