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标题：Artificial neural network based unity power factor corrector for single phase DC-DC converters
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作者：Hussain Attia
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2020
卷号：10
期号：4
页码：4145-4154
DOI：10.11591/ijece.v10i4.pp4145-4154
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Due to the negative effects of the non-linear semiconductor devices and the passive electrical components (inductor and capacitor) in the converter circuits, and that are deteriorating the power factor (PF) and total harmonics distortion (THD) of grid current, this study proposes a novel unity PF correction controller based on a new algorithm of neural network to improve the performance of a single phase boost DC-DC converter with respect to the mentioned concerns. The controller guarantees stable load voltage. The PF corrector, firstly measures the phase shift between grid voltage and grid current waveforms, then through a new artificial neural network (ANN) algorithm, a suitable duty cycle is predicted to guide and control the converter to reduce the phase shift between grid voltage and grid current as possible to have maximum PF which is unity PF, and to improve the THD level of grid current. The proposed system is simulated and evaluated via Simulink of MATLAB, the simulation results are collected at constant duty cycle and at controlled duty cycle through the proposed PF controller using different loads. The presented PF controller guarantees the unity power factor, and enhances the grid alternating current THD.
关键词：Unity power factor controller;neural network algorithm;duty cycle;DC-DC converter;variable loads;load voltage control;Total Harmonic Distortion;Simulink