Publication: An AN-GA controlled SEPIC converter for photovoltaic grid integration
Date
2019
Authors
Priyadarshi N.
Padmanaban S.
Holm-Nielsen J.B.
Ramachandaramurthy V.K.
Bhaskar M.S.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
In this paper, Artificial Neural Network (ANN) optimization with Genetic Algorithm (GA) is implemented. The optimized training to ANN is provide using Bayesian regulation. For this study, a Photovoltaic (PV) system has considered and optimal power tracking been interpreted with proper adjustment of ANN weights using GA approach, which reduces the Root Mean Square Error (RMSE). In this work, the single-ended primary-inductor converter (SEPIC) has been utilized for better power tracking from PV modules. SEPIC Converter accomplish with impedance matching power device and provides utmost PV power tracking. Space vector pulse width modulation-dSPACE interface been utilized as an inverter control. Simulated responses show the potency of the proposed system under sag, swell and varying loading conditions. � 2019 IEEE.
Description
Mean square error; Neural networks; Photovoltaic cells; Power converters; Power electronics; Vector spaces; Voltage control; Bayesian regulation; Grid; Photovoltaic; Photovoltaic systems; Root mean square errors; SEPIC; Single ended primary inductor converter (SEPIC); Space vector pulse width modulation; Genetic algorithms