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An adaptive neuro-fuzzy inference system employed cuk converter for PV applications

An Adaptive Neuro-Fuzzy Inference System based Intelligent Algorithm for Photovoltaic (PV) application proposed in this paper. Under the nonlinear behavior of the surroundings, the proposed algorithm achieves optimal power point (OPP) without prior system knowledge. Compared with other intelligent m...

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Main Authors: Priyadarshi, N., Padmanaban, S., Holm-Nielsen, J.B., Ramachandaramurthy, V.K., Bhaskar, M.S.
Format: Article
Language:English
Published: 2020
Online Access:http://dspace.uniten.edu.my/jspui/handle/123456789/13090
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spelling my.uniten.dspace-130902020-03-12T03:16:06Z An adaptive neuro-fuzzy inference system employed cuk converter for PV applications Priyadarshi, N. Padmanaban, S. Holm-Nielsen, J.B. Ramachandaramurthy, V.K. Bhaskar, M.S. An Adaptive Neuro-Fuzzy Inference System based Intelligent Algorithm for Photovoltaic (PV) application proposed in this paper. Under the nonlinear behavior of the surroundings, the proposed algorithm achieves optimal power point (OPP) without prior system knowledge. Compared with other intelligent methodologies, the proposed algorithm has low implementation cost, as it does not need any sensors to measure solar irradiance. The proposed algorithm provides proper training to the PV system under varying PV insolation. Modeled Cuk converter employed PV system is validate by simulated responses present in the paper. Inverter with Fuzzy logic control (FLC)-dSPACE board is also implement for sinusoidal current injection to the utility grid. © 2019 IEEE. 2020-02-03T03:30:19Z 2020-02-03T03:30:19Z 2019 Article http://dspace.uniten.edu.my/jspui/handle/123456789/13090 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 An Adaptive Neuro-Fuzzy Inference System based Intelligent Algorithm for Photovoltaic (PV) application proposed in this paper. Under the nonlinear behavior of the surroundings, the proposed algorithm achieves optimal power point (OPP) without prior system knowledge. Compared with other intelligent methodologies, the proposed algorithm has low implementation cost, as it does not need any sensors to measure solar irradiance. The proposed algorithm provides proper training to the PV system under varying PV insolation. Modeled Cuk converter employed PV system is validate by simulated responses present in the paper. Inverter with Fuzzy logic control (FLC)-dSPACE board is also implement for sinusoidal current injection to the utility grid. © 2019 IEEE.
format Article
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 adaptive neuro-fuzzy inference system employed cuk converter for PV applications
author_facet Priyadarshi, N.
Padmanaban, S.
Holm-Nielsen, J.B.
Ramachandaramurthy, V.K.
Bhaskar, M.S.
author_sort Priyadarshi, N.
title An adaptive neuro-fuzzy inference system employed cuk converter for PV applications
title_short An adaptive neuro-fuzzy inference system employed cuk converter for PV applications
title_full An adaptive neuro-fuzzy inference system employed cuk converter for PV applications
title_fullStr An adaptive neuro-fuzzy inference system employed cuk converter for PV applications
title_full_unstemmed An adaptive neuro-fuzzy inference system employed cuk converter for PV applications
title_sort adaptive neuro-fuzzy inference system employed cuk converter for pv applications
publishDate 2020
url http://dspace.uniten.edu.my/jspui/handle/123456789/13090
_version_ 1662758813386670080
score 13.214268

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