Electronic converter models implemented with radial basis function networks

Author

Moreno, M.A. ; Usaola, J.

Author_Institution

Univ. Carlos III de Madrid, Spain

Volume

2

fYear

2002

fDate

6-9 Oct. 2002

Firstpage

704

Abstract

Radial basis function (RBF) neural networks can be applied to the modelling of electronic converters. In this paper a new steady-state model of an uncontrolled bridge rectifier with capacitive DC smoothing is presented. The model considers the commutation effect and allows to obtain the harmonic currents injected by the converter in magnitude and angle. The model can be used to evaluate the harmonic distortion introduced in balanced networks by this device. The technique can be applied to any other converter or any other nonlinear load. Hence it is no more needed to know the analytical relationship between harmonic voltages and currents.

Keywords

bridge circuits; capacitors; commutation; fuzzy neural nets; harmonic distortion; power conversion harmonics; power convertors; power engineering computing; radial basis function networks; rectifying circuits; smoothing circuits; capacitive dc smoothing; commutation effect; current angle; current magnitude; electronic converter model implementation; harmonic currents injection; harmonic distortion evaluation; radial basis function neural network; steady-state model; uncontrolled bridge rectifier; Bridges; Frequency domain analysis; Harmonic analysis; Harmonic distortion; Load modeling; Neural networks; Radial basis function networks; Rectifiers; Smoothing methods; Steady-state;

fLanguage

English

Publisher

ieee

Conference_Titel

Harmonics and Quality of Power, 2002. 10th International Conference on

Print_ISBN

0-7803-7671-4

Type

conf

DOI

10.1109/ICHQP.2002.1221521

Filename

1221521