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Fault classification and location for distribution generation using artificial neural networks

Distributed power generation; Forecasting; Location; Machine learning; Neural networks; Bus networks; Distributed networks; Distribution generation; Fault classification; Fault distance; Fault sections; Location method; Three categories; Complex networks

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Bibliographic Details
Main Authors: Hong F.K., Keen Raymond W.J., Heong O.K., Mei Kuan T.
Other Authors: 57221910587
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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id my.uniten.dspace-25077
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spelling my.uniten.dspace-250772023-05-29T16:06:40Z Fault classification and location for distribution generation using artificial neural networks Hong F.K. Keen Raymond W.J. Heong O.K. Mei Kuan T. 57221910587 55193255600 55096903900 57220873063 Distributed power generation; Forecasting; Location; Machine learning; Neural networks; Bus networks; Distributed networks; Distribution generation; Fault classification; Fault distance; Fault sections; Location method; Three categories; Complex networks With the proliferation of distributed generation (DG), the distributed network had become more complex. Such complexity will lead to difficulty for fault location in the distributed network. It may degrade the precision of existing fault location methods. Therefore, this paper will investigate the impact of distributed generation toward machine learning (ML) based fault location. Three categories of fault location had been tested which is fault type prediction, fault section prediction, and fault distance prediction with and without DG presence. The accuracy of machine learning based fault location is verified in IEEE 16 bus network and the impact due to the presence of DG, represented using photovoltaic (PV) generator is discussed in detail. � 2020 IEEE. Final 2023-05-29T08:06:40Z 2023-05-29T08:06:40Z 2020 Conference Paper 10.1109/PECon48942.2020.9314535 2-s2.0-85100588405 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100588405&doi=10.1109%2fPECon48942.2020.9314535&partnerID=40&md5=7192ad3644e48e012973b59138838f44 https://irepository.uniten.edu.my/handle/123456789/25077 9314535 315 320 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Distributed power generation; Forecasting; Location; Machine learning; Neural networks; Bus networks; Distributed networks; Distribution generation; Fault classification; Fault distance; Fault sections; Location method; Three categories; Complex networks
author2 57221910587
author_facet 57221910587
Hong F.K.
Keen Raymond W.J.
Heong O.K.
Mei Kuan T.
format Conference Paper
author Hong F.K.
Keen Raymond W.J.
Heong O.K.
Mei Kuan T.
spellingShingle Hong F.K.
Keen Raymond W.J.
Heong O.K.
Mei Kuan T.
Fault classification and location for distribution generation using artificial neural networks
author_sort Hong F.K.
title Fault classification and location for distribution generation using artificial neural networks
title_short Fault classification and location for distribution generation using artificial neural networks
title_full Fault classification and location for distribution generation using artificial neural networks
title_fullStr Fault classification and location for distribution generation using artificial neural networks
title_full_unstemmed Fault classification and location for distribution generation using artificial neural networks
title_sort fault classification and location for distribution generation using artificial neural networks
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806424126321065984
score 13.214268

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