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Optimization of photovoltaic energy harvesting using artificial neural network

This paper proposes artificial neural network (ANN) based maximum power point tracking (MPPT) controller to maximize the energy harvested by a grid-connected photovoltaic (PV) system under various environmental conditions. Due to the non-linear characteristics, PV system will exhibit multiple peaks...

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Main Authors: Min, Keng Tan, Kit, Guan Lim, Norman Lim, Soo, Siang Yang, Nurul Izyan Kamaruddin, Tze, Kenneth Kin Teo
Format: Proceedings
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc 2021
Subjects:
QA1-43 General
TD1-1066 Environmental technology. Sanitary engineering
Online Access:https://eprints.ums.edu.my/id/eprint/32501/1/Optimization%20of%20photovoltaic%20energy%20harvesting%20using%20artificial%20neural%20network.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/32501/2/Optimization%20of%20photovoltaic%20energy%20harvesting%20using%20artificial%20neural%20network.pdf
https://eprints.ums.edu.my/id/eprint/32501/
https://ieeexplore.ieee.org/document/9573886
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spelling my.ums.eprints.325012022-05-03T11:56:07Z https://eprints.ums.edu.my/id/eprint/32501/ Optimization of photovoltaic energy harvesting using artificial neural network Min, Keng Tan Kit, Guan Lim Norman Lim Soo, Siang Yang Nurul Izyan Kamaruddin Tze, Kenneth Kin Teo QA1-43 General TD1-1066 Environmental technology. Sanitary engineering This paper proposes artificial neural network (ANN) based maximum power point tracking (MPPT) controller to maximize the energy harvested by a grid-connected photovoltaic (PV) system under various environmental conditions. Due to the non-linear characteristics, PV system will exhibit multiple peaks when the PV array receives non-uniform irradiance. As such, the conventional perturb and observe (P&O) MPPT controller will be trapped at local maximum power point (MPP). Therefore, this paper aims to integrate ANN into MPPT controller to improve the effectiveness of the MPPT controller in tracking the global MPP. The effectiveness of the proposed method is tested under uniform and non-uniform irradiance conditions, and the performances are compared with the conventional P&O. The simulation results show the proposed method able to track the global MPP even the PV system exhibits multiple peaks under non-uniform condition, whereas the conventional P&O is trapped at local MPP. Thus, the proposed algorithm is able to harvest much energy as compared to the conventional method. Institute of Electrical and Electronics Engineers Inc 2021-09-13 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/32501/1/Optimization%20of%20photovoltaic%20energy%20harvesting%20using%20artificial%20neural%20network.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/32501/2/Optimization%20of%20photovoltaic%20energy%20harvesting%20using%20artificial%20neural%20network.pdf Min, Keng Tan and Kit, Guan Lim and Norman Lim and Soo, Siang Yang and Nurul Izyan Kamaruddin and Tze, Kenneth Kin Teo (2021) Optimization of photovoltaic energy harvesting using artificial neural network. https://ieeexplore.ieee.org/document/9573886
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic QA1-43 General
TD1-1066 Environmental technology. Sanitary engineering
spellingShingle QA1-43 General
TD1-1066 Environmental technology. Sanitary engineering
Min, Keng Tan
Kit, Guan Lim
Norman Lim
Soo, Siang Yang
Nurul Izyan Kamaruddin
Tze, Kenneth Kin Teo
Optimization of photovoltaic energy harvesting using artificial neural network
description This paper proposes artificial neural network (ANN) based maximum power point tracking (MPPT) controller to maximize the energy harvested by a grid-connected photovoltaic (PV) system under various environmental conditions. Due to the non-linear characteristics, PV system will exhibit multiple peaks when the PV array receives non-uniform irradiance. As such, the conventional perturb and observe (P&O) MPPT controller will be trapped at local maximum power point (MPP). Therefore, this paper aims to integrate ANN into MPPT controller to improve the effectiveness of the MPPT controller in tracking the global MPP. The effectiveness of the proposed method is tested under uniform and non-uniform irradiance conditions, and the performances are compared with the conventional P&O. The simulation results show the proposed method able to track the global MPP even the PV system exhibits multiple peaks under non-uniform condition, whereas the conventional P&O is trapped at local MPP. Thus, the proposed algorithm is able to harvest much energy as compared to the conventional method.
format Proceedings
author Min, Keng Tan
Kit, Guan Lim
Norman Lim
Soo, Siang Yang
Nurul Izyan Kamaruddin
Tze, Kenneth Kin Teo
author_facet Min, Keng Tan
Kit, Guan Lim
Norman Lim
Soo, Siang Yang
Nurul Izyan Kamaruddin
Tze, Kenneth Kin Teo
author_sort Min, Keng Tan
title Optimization of photovoltaic energy harvesting using artificial neural network
title_short Optimization of photovoltaic energy harvesting using artificial neural network
title_full Optimization of photovoltaic energy harvesting using artificial neural network
title_fullStr Optimization of photovoltaic energy harvesting using artificial neural network
title_full_unstemmed Optimization of photovoltaic energy harvesting using artificial neural network
title_sort optimization of photovoltaic energy harvesting using artificial neural network
publisher Institute of Electrical and Electronics Engineers Inc
publishDate 2021
url https://eprints.ums.edu.my/id/eprint/32501/1/Optimization%20of%20photovoltaic%20energy%20harvesting%20using%20artificial%20neural%20network.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/32501/2/Optimization%20of%20photovoltaic%20energy%20harvesting%20using%20artificial%20neural%20network.pdf
https://eprints.ums.edu.my/id/eprint/32501/
https://ieeexplore.ieee.org/document/9573886
_version_ 1760231034994032640
score 13.211869

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