Comparison of feed forward and cascade neural network for harmonic current estimation in power electronic converter

Abstract

This paper presents harmonic current estimation using neural network for a power electronic converter. Three types of popular neural architectures namely single hidden layered Feedforward architecture, multi hidden layered Feedforward neural architecture, cascade architecture are considered for investigation. The non-linear load namely diode bridge uncontrolled rectifier with resistive inductive (RL) load is chosen for study. All the three architectures are trained and tested using MATLAB simulation. The performance of three types of neural architectures is compared in terms of accuracy and complexity for harmonic current estimation. The suitable neural architecture is identified for harmonic current estimation. The results obtained are presented.