Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter

Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three int...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Maria I. S. Guerra, Fábio M. Ugulino de Araújo, Mahmoud Dhimish, Romênia G. Vieira
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	photovoltaic systems MPPT ANN fuzzy ANFIS power recovery Technology T
Acceso en línea:	https://doaj.org/article/5ebf36f10e3b4dfb8a974696db564a32
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5ebf36f10e3b4dfb8a974696db564a32
record_format	dspace
spelling	oai:doaj.org-article:5ebf36f10e3b4dfb8a974696db564a322021-11-25T17:25:40ZAssessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter10.3390/en142274531996-1073https://doaj.org/article/5ebf36f10e3b4dfb8a974696db564a322021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7453https://doaj.org/toc/1996-1073Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter’s switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power.Maria I. S. GuerraFábio M. Ugulino de AraújoMahmoud DhimishRomênia G. VieiraMDPI AGarticlephotovoltaic systemsMPPTANNfuzzyANFISpower recoveryTechnologyTENEnergies, Vol 14, Iss 7453, p 7453 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	photovoltaic systems MPPT ANN fuzzy ANFIS power recovery Technology T
spellingShingle	photovoltaic systems MPPT ANN fuzzy ANFIS power recovery Technology T Maria I. S. Guerra Fábio M. Ugulino de Araújo Mahmoud Dhimish Romênia G. Vieira Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter
description	Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter’s switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power.
format	article
author	Maria I. S. Guerra Fábio M. Ugulino de Araújo Mahmoud Dhimish Romênia G. Vieira
author_facet	Maria I. S. Guerra Fábio M. Ugulino de Araújo Mahmoud Dhimish Romênia G. Vieira
author_sort	Maria I. S. Guerra
title	Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter
title_short	Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter
title_full	Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter
title_fullStr	Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter
title_full_unstemmed	Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter
title_sort	assessing maximum power point tracking intelligent techniques on a pv system with a buck–boost converter
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5ebf36f10e3b4dfb8a974696db564a32
work_keys_str_mv	AT mariaisguerra assessingmaximumpowerpointtrackingintelligenttechniquesonapvsystemwithabuckboostconverter AT fabiomugulinodearaujo assessingmaximumpowerpointtrackingintelligenttechniquesonapvsystemwithabuckboostconverter AT mahmouddhimish assessingmaximumpowerpointtrackingintelligenttechniquesonapvsystemwithabuckboostconverter AT romeniagvieira assessingmaximumpowerpointtrackingintelligenttechniquesonapvsystemwithabuckboostconverter
_version_	1718412352898990080

Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter

Ejemplares similares