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Malaysia solar energy experience: intelligent fault location algorithm for unbalanced radial distribution network including PV systems

Due to environmental issues and the upward trend of fossil fuel prices, the study of renewable energy (RE) based generation and their effects on the electrical system has become an important part of the government's energy policies and university projects. In RE generation, as solar photovoltai...

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Main Authors: Farzan, Payam, Izadi, Mahdi, Gomes, Chandima, Hesamian, Mohammad Hesam
Format: Article
Language:English
Published: AIP Publishing LLC 2016
Online Access:http://psasir.upm.edu.my/id/eprint/54739/1/Malaysia%20solar%20energy%20experience.pdf
http://psasir.upm.edu.my/id/eprint/54739/
https://aip.scitation.org/doi/abs/10.1063/1.4961589
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spelling my.upm.eprints.547392018-04-20T07:26:29Z http://psasir.upm.edu.my/id/eprint/54739/ Malaysia solar energy experience: intelligent fault location algorithm for unbalanced radial distribution network including PV systems Farzan, Payam Izadi, Mahdi Gomes, Chandima Hesamian, Mohammad Hesam Due to environmental issues and the upward trend of fossil fuel prices, the study of renewable energy (RE) based generation and their effects on the electrical system has become an important part of the government's energy policies and university projects. In RE generation, as solar photovoltaic (PV) systems are modular, silent, and transportable and demonstrate ease of installation, they have attracted a greater amount of attention specifically in those areas which receive considerable average solar radiation per day such as Malaysia. However, connecting solar PV farms to the grid like any other distributed generation (DG) units poses serious issues which arise in the distribution network. This paper presents a novel fault location algorithm based on the recording of short circuit power values at the primary substation of unbalanced radial distribution networks including PV systems. The recorded values are evaluated by a designed and tuned multi-layer feed forward neural network and the fault distances from the source are estimated accordingly. In order to highlight the accuracy of the presented method, the scenario is also repeated by recording the peak values of short circuit current which have been mostly used in the published intelligent fault location studies and the obtained results via two different values are compared with each other. The results reveal that the presented algorithm using a small scale input set is able to precisely locate different fault types in the unbalanced distribution networks including DG units. AIP Publishing LLC 2016 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/54739/1/Malaysia%20solar%20energy%20experience.pdf Farzan, Payam and Izadi, Mahdi and Gomes, Chandima and Hesamian, Mohammad Hesam (2016) Malaysia solar energy experience: intelligent fault location algorithm for unbalanced radial distribution network including PV systems. Journal of Renewable and Sustainable Energy, 8 (4). pp. 1-16. ISSN 1941-7012 https://aip.scitation.org/doi/abs/10.1063/1.4961589 10.1063/1.4961589
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Due to environmental issues and the upward trend of fossil fuel prices, the study of renewable energy (RE) based generation and their effects on the electrical system has become an important part of the government's energy policies and university projects. In RE generation, as solar photovoltaic (PV) systems are modular, silent, and transportable and demonstrate ease of installation, they have attracted a greater amount of attention specifically in those areas which receive considerable average solar radiation per day such as Malaysia. However, connecting solar PV farms to the grid like any other distributed generation (DG) units poses serious issues which arise in the distribution network. This paper presents a novel fault location algorithm based on the recording of short circuit power values at the primary substation of unbalanced radial distribution networks including PV systems. The recorded values are evaluated by a designed and tuned multi-layer feed forward neural network and the fault distances from the source are estimated accordingly. In order to highlight the accuracy of the presented method, the scenario is also repeated by recording the peak values of short circuit current which have been mostly used in the published intelligent fault location studies and the obtained results via two different values are compared with each other. The results reveal that the presented algorithm using a small scale input set is able to precisely locate different fault types in the unbalanced distribution networks including DG units.
format Article
author Farzan, Payam
Izadi, Mahdi
Gomes, Chandima
Hesamian, Mohammad Hesam
spellingShingle Farzan, Payam
Izadi, Mahdi
Gomes, Chandima
Hesamian, Mohammad Hesam
Malaysia solar energy experience: intelligent fault location algorithm for unbalanced radial distribution network including PV systems
author_facet Farzan, Payam
Izadi, Mahdi
Gomes, Chandima
Hesamian, Mohammad Hesam
author_sort Farzan, Payam
title Malaysia solar energy experience: intelligent fault location algorithm for unbalanced radial distribution network including PV systems
title_short Malaysia solar energy experience: intelligent fault location algorithm for unbalanced radial distribution network including PV systems
title_full Malaysia solar energy experience: intelligent fault location algorithm for unbalanced radial distribution network including PV systems
title_fullStr Malaysia solar energy experience: intelligent fault location algorithm for unbalanced radial distribution network including PV systems
title_full_unstemmed Malaysia solar energy experience: intelligent fault location algorithm for unbalanced radial distribution network including PV systems
title_sort malaysia solar energy experience: intelligent fault location algorithm for unbalanced radial distribution network including pv systems
publisher AIP Publishing LLC
publishDate 2016
url http://psasir.upm.edu.my/id/eprint/54739/1/Malaysia%20solar%20energy%20experience.pdf
http://psasir.upm.edu.my/id/eprint/54739/
https://aip.scitation.org/doi/abs/10.1063/1.4961589
_version_ 1643835695981133824
score 13.211869

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