Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking

Photovoltaic energy is one of the most renowned sources of renewable energy. Its major drawback, however, is the low efficiency of ultra-violet to electrical energy conversion. Irradiance and temperature are the major factors that determine its ability to achieve maximum power output. Maximum...

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Main Author:	Dira, Yasir Sabah
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Photovoltaic power systems
Online Access:	http://psasir.upm.edu.my/id/eprint/75444/1/FK%202018%20123%20-%20IR.pdf
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id	my-upm-ir.75444
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spelling	my-upm-ir.754442019-11-13T07:28:43Z Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking 2018-03 Dira, Yasir Sabah Photovoltaic energy is one of the most renowned sources of renewable energy. Its major drawback, however, is the low efficiency of ultra-violet to electrical energy conversion. Irradiance and temperature are the major factors that determine its ability to achieve maximum power output. Maximum power point tracking (MPPT) is developed in photovoltaic systems to maintain the maximum power output produced by its source. A boost DC-DC converter with maximum power point tracking algorithm aids in operating at the desired voltage level. From different MPPT techniques previously proposed, the online sequential extreme learning machine algorithm and conventional perturb and observe are combined together as a proposed MPPT algorithm. This combination is capable of extracting energy at the maximum operating level of a photovoltaic module. The simulation work covers modelling of the photovoltaic module, and the boost DC-DC converter and power LED light as a load, with maximum power point tracking algorithm to form a photovoltaic system. This system was evaluated under the actual environmental data based on location and dynamic MPPT efficiency tests. For comparison purpose, the conventional extreme learning machine and modified P&O were modelled as well. Several factors will be triggered on the solar module performance, and the PV module will be degraded over time. In this thesis, the proposed method will be emulated under the degradation of maximum output PV current. The diode ideality factor was chosen to evaluate the PV output current degradation.System elements are individually modelled in MATLAB/M-File and then connected to assess performance under different environmental conditions. The simulated results of the complete PV system show that the performances of the PV module using the proposed MPPT technique provide better output power when compared with the conventional ELM and modified P&O. It yields not only a reduction in convergence time to track the maximum power point but also significant output power when subjected to slow and rapid solar irradiance changes. Photovoltaic power systems 2018-03 Thesis http://psasir.upm.edu.my/id/eprint/75444/ http://psasir.upm.edu.my/id/eprint/75444/1/FK%202018%20123%20-%20IR.pdf text en public masters Universiti Putra Malaysia Photovoltaic power systems
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Photovoltaic power systems
spellingShingle	Photovoltaic power systems Dira, Yasir Sabah Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking
description	Photovoltaic energy is one of the most renowned sources of renewable energy. Its major drawback, however, is the low efficiency of ultra-violet to electrical energy conversion. Irradiance and temperature are the major factors that determine its ability to achieve maximum power output. Maximum power point tracking (MPPT) is developed in photovoltaic systems to maintain the maximum power output produced by its source. A boost DC-DC converter with maximum power point tracking algorithm aids in operating at the desired voltage level. From different MPPT techniques previously proposed, the online sequential extreme learning machine algorithm and conventional perturb and observe are combined together as a proposed MPPT algorithm. This combination is capable of extracting energy at the maximum operating level of a photovoltaic module. The simulation work covers modelling of the photovoltaic module, and the boost DC-DC converter and power LED light as a load, with maximum power point tracking algorithm to form a photovoltaic system. This system was evaluated under the actual environmental data based on location and dynamic MPPT efficiency tests. For comparison purpose, the conventional extreme learning machine and modified P&O were modelled as well. Several factors will be triggered on the solar module performance, and the PV module will be degraded over time. In this thesis, the proposed method will be emulated under the degradation of maximum output PV current. The diode ideality factor was chosen to evaluate the PV output current degradation.System elements are individually modelled in MATLAB/M-File and then connected to assess performance under different environmental conditions. The simulated results of the complete PV system show that the performances of the PV module using the proposed MPPT technique provide better output power when compared with the conventional ELM and modified P&O. It yields not only a reduction in convergence time to track the maximum power point but also significant output power when subjected to slow and rapid solar irradiance changes.
format	Thesis
qualification_level	Master's degree
author	Dira, Yasir Sabah
author_facet	Dira, Yasir Sabah
author_sort	Dira, Yasir Sabah
title	Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking
title_short	Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking
title_full	Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking
title_fullStr	Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking
title_full_unstemmed	Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking
title_sort	combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking
granting_institution	Universiti Putra Malaysia
publishDate	2018
url	http://psasir.upm.edu.my/id/eprint/75444/1/FK%202018%20123%20-%20IR.pdf
_version_	1747813052399484928

Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking

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