Journal of Electrical Engineering : Volume 19 / 2018 - Edition : 3

WIDESPREAD ARTIFICIAL NEURAL NETWORK (ANN) STRUCTURES FOR CURRENT HARMONICS EXTRACTION

Authors:
Mehmet Byk
Mustafa İnci
Turgay İbriki
Mehmet Tmay
Domain:
power electronics
Abstract:
The extraction of current harmonics produced by nonlinear loads in the power system is significant issue for compensating them fast and accurately. In this paper, the main contribution is that widespread artificial neural network (ANN) structures are used to acquire harmonic components generated by a six-pulse uncontrolled rectifier. For this purpose, ANN including computational algorithms operate according to the functionality and structural scheme of biologic neurons. Among ANN network structures, radial basis function (RBF) and multilayer feedforward (MLF) networks are two widespread used architectures for harmonic extraction in literature. Thus, this paper examines harmonic extraction performances of these common used ANN structures by using MATLAB/Simulink environment. In this way, the networks are firstly trained by using sample input data and output data with respect of training algorithms. In order to train the networks, some training algorithms are applied, and the optimal network and algorithm are emphasized
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