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Title: An AN-GA controlled SEPIC converter for photovoltaic grid integration
Authors: Priyadarshi, N. 
Padmanaban, S. 
Holm-Nielsen, J.B. 
Ramachandaramurthy, V.K. 
Bhaskar, M.S. 
Issue Date: 2019
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.
DOI: 10.1109/CPE.2019.8862395
Appears in Collections:UNITEN Scholarly Publication

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