Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.

In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniqu...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hazlee Azil Illias, Ming Ming Lim, Ab Halim Abu Bakar, Hazlie Mokhlis, Sanuri Ishak, Mohd Dzaki Mohd Amir
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/91ad42d354074a1c8a506c6a63d92427
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:91ad42d354074a1c8a506c6a63d92427
record_format	dspace
spelling	oai:doaj.org-article:91ad42d354074a1c8a506c6a63d924272021-12-02T20:09:45ZClassification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.1932-620310.1371/journal.pone.0253967https://doaj.org/article/91ad42d354074a1c8a506c6a63d924272021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253967https://doaj.org/toc/1932-6203In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.Hazlee Azil IlliasMing Ming LimAb Halim Abu BakarHazlie MokhlisSanuri IshakMohd Dzaki Mohd AmirPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0253967 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hazlee Azil Illias Ming Ming Lim Ab Halim Abu Bakar Hazlie Mokhlis Sanuri Ishak Mohd Dzaki Mohd Amir Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.
description	In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.
format	article
author	Hazlee Azil Illias Ming Ming Lim Ab Halim Abu Bakar Hazlie Mokhlis Sanuri Ishak Mohd Dzaki Mohd Amir
author_facet	Hazlee Azil Illias Ming Ming Lim Ab Halim Abu Bakar Hazlie Mokhlis Sanuri Ishak Mohd Dzaki Mohd Amir
author_sort	Hazlee Azil Illias
title	Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.
title_short	Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.
title_full	Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.
title_fullStr	Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.
title_full_unstemmed	Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.
title_sort	classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/91ad42d354074a1c8a506c6a63d92427
work_keys_str_mv	AT hazleeazilillias classificationofabnormallocationinmediumvoltageswitchgearsusinghybridgravitationalsearchalgorithmartificialintelligence AT mingminglim classificationofabnormallocationinmediumvoltageswitchgearsusinghybridgravitationalsearchalgorithmartificialintelligence AT abhalimabubakar classificationofabnormallocationinmediumvoltageswitchgearsusinghybridgravitationalsearchalgorithmartificialintelligence AT hazliemokhlis classificationofabnormallocationinmediumvoltageswitchgearsusinghybridgravitationalsearchalgorithmartificialintelligence AT sanuriishak classificationofabnormallocationinmediumvoltageswitchgearsusinghybridgravitationalsearchalgorithmartificialintelligence AT mohddzakimohdamir classificationofabnormallocationinmediumvoltageswitchgearsusinghybridgravitationalsearchalgorithmartificialintelligence
_version_	1718375076244488192

Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.

Ejemplares similares