Multiple peaks tracking for photovoltaic system using particle swarm optimization with artificial neural network algorithm

Photovoltaic (PV) array may receive different level of solar irradiance, such as partially shaded by clouds or nearby building. Multiple peak power points occur when PV module is under partially shaded conditions, which would significantly reduce the energy produced by PV without proper control. The...

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Main Author:	Ngan, Mei Shan
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/78292/1/NganMeiShanMFKE20131.pdf
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spelling	my-utm-ep.782922018-08-03T08:47:10Z Multiple peaks tracking for photovoltaic system using particle swarm optimization with artificial neural network algorithm 2013-11 Ngan, Mei Shan TK Electrical engineering. Electronics Nuclear engineering Photovoltaic (PV) array may receive different level of solar irradiance, such as partially shaded by clouds or nearby building. Multiple peak power points occur when PV module is under partially shaded conditions, which would significantly reduce the energy produced by PV without proper control. Therefore, Maximum Power Point Tracking (MPPT) algorithm is used to extract maximum available PV power from the PV array. However, most of the conventional MPPT algorithms are incapable to detect global peak power point with the presence of several local peaks. A hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed in this thesis to detect the global peak power. The PV system which consists of PV array, dc-dc boost converter and a resistive load, were simulated using MATLAB/Simulink. The performance of the proposed algorithm is compared with that of the standard PSO algorithm. The proposed algorithm is tested and verified by hardware experiment. The simulation results and the experimental results are compared and discussed. It shows that the proposed algorithm performs well to detect the global peak of the PV array under partially shaded conditions. In this work, the tracking efficiency of the proposed algorithm is in the range of 96.8 % to 99.7 %. 2013-11 Thesis http://eprints.utm.my/id/eprint/78292/ http://eprints.utm.my/id/eprint/78292/1/NganMeiShanMFKE20131.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:79539 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Ngan, Mei Shan Multiple peaks tracking for photovoltaic system using particle swarm optimization with artificial neural network algorithm
description	Photovoltaic (PV) array may receive different level of solar irradiance, such as partially shaded by clouds or nearby building. Multiple peak power points occur when PV module is under partially shaded conditions, which would significantly reduce the energy produced by PV without proper control. Therefore, Maximum Power Point Tracking (MPPT) algorithm is used to extract maximum available PV power from the PV array. However, most of the conventional MPPT algorithms are incapable to detect global peak power point with the presence of several local peaks. A hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed in this thesis to detect the global peak power. The PV system which consists of PV array, dc-dc boost converter and a resistive load, were simulated using MATLAB/Simulink. The performance of the proposed algorithm is compared with that of the standard PSO algorithm. The proposed algorithm is tested and verified by hardware experiment. The simulation results and the experimental results are compared and discussed. It shows that the proposed algorithm performs well to detect the global peak of the PV array under partially shaded conditions. In this work, the tracking efficiency of the proposed algorithm is in the range of 96.8 % to 99.7 %.
format	Thesis
qualification_level	Master's degree
author	Ngan, Mei Shan
author_facet	Ngan, Mei Shan
author_sort	Ngan, Mei Shan
title	Multiple peaks tracking for photovoltaic system using particle swarm optimization with artificial neural network algorithm
title_short	Multiple peaks tracking for photovoltaic system using particle swarm optimization with artificial neural network algorithm
title_full	Multiple peaks tracking for photovoltaic system using particle swarm optimization with artificial neural network algorithm
title_fullStr	Multiple peaks tracking for photovoltaic system using particle swarm optimization with artificial neural network algorithm
title_full_unstemmed	Multiple peaks tracking for photovoltaic system using particle swarm optimization with artificial neural network algorithm
title_sort	multiple peaks tracking for photovoltaic system using particle swarm optimization with artificial neural network algorithm
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/78292/1/NganMeiShanMFKE20131.pdf
_version_	1747817953871527936

Multiple peaks tracking for photovoltaic system using particle swarm optimization with artificial neural network algorithm

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