Modeling Nonlinear Inductor, Resistor, and Buck Converter Using NARX Neural Network

Hayder D.  Almukhtar; Bayadir A.  Issa; Mohammed K.   Alkhafaji

doi:10.30572/2018/KJE/160411

Authors

Hayder D. Almukhtar EETRG, EET Department, BETC, Southern Technical University, Basrah, Iraq
Bayadir A. Issa EETRG, EET Department, BETC, Southern Technical University, Basrah, Iraq
Mohammed K. Alkhafaji EETRG, EET Department, BETC, Southern Technical University, Basrah, Iraq

DOI:

https://doi.org/10.30572/2018/KJE/160411

Keywords:

Nonlinear inductor, nonlinear resistor, Buck converter, Artificial Neural Networks

Abstract

This study investigates the methodology of using the capability of Artificial Neural Networks (ANNs) as an alternative approach for modeling nonlinear inductors, nonlinear resistors, and power electronic circuits in electrical power systems. While traditional physical modeling approaches face challenges due to the complex dynamic behavior of these elements and power electronic circuits. In comparison, physical models offer accuracy but require extensive time and detailed parameter knowledge. In contrast, the ANN-based methodology is considered a powerful and efficient solution for enabling approximation of any nonlinear component, simple implementation, and fast computations. The research explores diverse ANN architectures (nonlinear autoregressive network with exogenous inputs (NARX), A feedforward neural network (FNN), and A recurrent neural network (RNN)) to model nonlinear inductor, resistors, diode, and a Buck converter. The investigation demonstrates the effectiveness and versatility of ANN-based modeling in predicting and optimizing nonlinear behavior for the nonlinear components

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