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An AN-GA controlled SEPIC converter for photovoltaic grid integration

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...

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Main Authors: Priyadarshi, N., Padmanaban, S., Holm-Nielsen, J.B., Ramachandaramurthy, V.K., Bhaskar, M.S.
Format: Conference Paper
Language:English
Published: 2020
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spelling my.uniten.dspace-130922020-03-17T04:43:55Z An AN-GA controlled SEPIC converter for photovoltaic grid integration Priyadarshi, N. Padmanaban, S. Holm-Nielsen, J.B. Ramachandaramurthy, V.K. Bhaskar, M.S. 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. 2020-02-03T03:30:20Z 2020-02-03T03:30:20Z 2019 Conference Paper 10.1109/CPE.2019.8862395 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description 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.
format Conference Paper
author Priyadarshi, N.
Padmanaban, S.
Holm-Nielsen, J.B.
Ramachandaramurthy, V.K.
Bhaskar, M.S.
spellingShingle Priyadarshi, N.
Padmanaban, S.
Holm-Nielsen, J.B.
Ramachandaramurthy, V.K.
Bhaskar, M.S.
An AN-GA controlled SEPIC converter for photovoltaic grid integration
author_facet Priyadarshi, N.
Padmanaban, S.
Holm-Nielsen, J.B.
Ramachandaramurthy, V.K.
Bhaskar, M.S.
author_sort Priyadarshi, N.
title An AN-GA controlled SEPIC converter for photovoltaic grid integration
title_short An AN-GA controlled SEPIC converter for photovoltaic grid integration
title_full An AN-GA controlled SEPIC converter for photovoltaic grid integration
title_fullStr An AN-GA controlled SEPIC converter for photovoltaic grid integration
title_full_unstemmed An AN-GA controlled SEPIC converter for photovoltaic grid integration
title_sort an-ga controlled sepic converter for photovoltaic grid integration
publishDate 2020
_version_ 1662758813665591296
score 13.235796

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