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

Farzan, P; Izadi, M; Gomes, C; Hesamian, M

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

Farzan, P Izadi, M Gomes, C Hesamian, M

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Publisher:: AIP Publishing
Publication Type:: Journal Article
Citation:: Journal of Renewable and Sustainable Energy, 2016, 8, (4), pp. 045503
Issue Date:: 2016-07-01

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Field	Value	Language
dc.contributor.author	Farzan, P
dc.contributor.author	Izadi, M
dc.contributor.author	Gomes, C
dc.contributor.author	Hesamian, M https://orcid.org/0000-0001-7877-0804
dc.date.accessioned	2022-10-24T23:57:35Z
dc.date.available	2022-10-24T23:57:35Z
dc.date.issued	2016-07-01
dc.identifier.citation	Journal of Renewable and Sustainable Energy, 2016, 8, (4), pp. 045503
dc.identifier.issn	1941-7012
dc.identifier.issn	1941-7012
dc.identifier.uri	http://hdl.handle.net/10453/162648
dc.description.abstract	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.
dc.language	en
dc.publisher	AIP Publishing
dc.relation.ispartof	Journal of Renewable and Sustainable Energy
dc.relation.isbasedon	10.1063/1.4961589
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0502 Environmental Science and Management, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.title	Malaysia solar energy experience: Intelligent fault location algorithm for unbalanced radial distribution network including PV systems
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	0502 Environmental Science and Management
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-10-24T23:57:34Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	4

Abstract:

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.

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http://hdl.handle.net/10453/162648