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...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | Proceedings |
Language: | English English |
Published: |
Institute of Electrical and Electronics Engineers Inc
2021
|
Subjects: | |
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 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.ums.eprints.32501 |
---|---|
record_format |
eprints |
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 |
13.18916 |