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Location of voltage sag source by using artificial neural network

Power quality (PQ) is a major concern for number of electrical equipment such as sophisticated electronics equipment, high efficiency variable speed drive (VSD) and power electronic controller. The most common power quality event is the voltage sag. The objective is to estimate the location of...

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Main Authors: Wagiman, Khairul Rijal, H. Shareef, S. N. Khalidb
Format: Article
Language:English
Published: Penerbit UTM Press 2017
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/7201/1/J14061_80e46e38c44d85d6637a8b387dbecb35.pdf
http://eprints.uthm.edu.my/7201/
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spelling my.uthm.eprints.72012022-06-21T02:07:39Z http://eprints.uthm.edu.my/7201/ Location of voltage sag source by using artificial neural network Wagiman, Khairul Rijal H. Shareef, S. N. Khalidb T Technology (General) Power quality (PQ) is a major concern for number of electrical equipment such as sophisticated electronics equipment, high efficiency variable speed drive (VSD) and power electronic controller. The most common power quality event is the voltage sag. The objective is to estimate the location of voltage sag source using ANN. In this paper, the multi-monitor based method is used. Based on the simulation results, the voltage deviation (VD) index of voltage sag is calculated and assigned as a training data for ANN. The Radial Basis Function Network (RBFN) is used due to its superior performances (lower training time and errors). The three types of performance analysis considered are coefficient of determination (R2), root mean square error (RMSE) and sum of square error (SSE). The RBFN is developed by using MATLAB software. The proposed method is tested on the CIVANLAR distribution test system and the Permas Jaya distribution network. The voltage sags are simulated using Power World software which is a common simulation tool for power system analysis. The asymmetrical fault namely line to ground (LG) fault, double line to ground (LLG) fault and line to line (LL) fault are applied in the simulation. Based on the simulation results of voltage sag analysis, the highest VD is contributed by LLG for both test systems. Based on the proposed RBFN results, the best performance analysis are R2, RMSE and SSE of 0.9999, 5.24E-04 and 3.90E-05, respectively. Based on the results, the highest VD shows the location of voltage sag source in that system. The proposed RBFN accurately identifies the location of voltage sag source for both test systems. Penerbit UTM Press 2017 Article PeerReviewed text en http://eprints.uthm.edu.my/7201/1/J14061_80e46e38c44d85d6637a8b387dbecb35.pdf Wagiman, Khairul Rijal and H. Shareef, S. N. Khalidb (2017) Location of voltage sag source by using artificial neural network. Jurnal Teknologi, 79 (2). pp. 11-30.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Wagiman, Khairul Rijal
H. Shareef, S. N. Khalidb
Location of voltage sag source by using artificial neural network
description Power quality (PQ) is a major concern for number of electrical equipment such as sophisticated electronics equipment, high efficiency variable speed drive (VSD) and power electronic controller. The most common power quality event is the voltage sag. The objective is to estimate the location of voltage sag source using ANN. In this paper, the multi-monitor based method is used. Based on the simulation results, the voltage deviation (VD) index of voltage sag is calculated and assigned as a training data for ANN. The Radial Basis Function Network (RBFN) is used due to its superior performances (lower training time and errors). The three types of performance analysis considered are coefficient of determination (R2), root mean square error (RMSE) and sum of square error (SSE). The RBFN is developed by using MATLAB software. The proposed method is tested on the CIVANLAR distribution test system and the Permas Jaya distribution network. The voltage sags are simulated using Power World software which is a common simulation tool for power system analysis. The asymmetrical fault namely line to ground (LG) fault, double line to ground (LLG) fault and line to line (LL) fault are applied in the simulation. Based on the simulation results of voltage sag analysis, the highest VD is contributed by LLG for both test systems. Based on the proposed RBFN results, the best performance analysis are R2, RMSE and SSE of 0.9999, 5.24E-04 and 3.90E-05, respectively. Based on the results, the highest VD shows the location of voltage sag source in that system. The proposed RBFN accurately identifies the location of voltage sag source for both test systems.
format Article
author Wagiman, Khairul Rijal
H. Shareef, S. N. Khalidb
author_facet Wagiman, Khairul Rijal
H. Shareef, S. N. Khalidb
author_sort Wagiman, Khairul Rijal
title Location of voltage sag source by using artificial neural network
title_short Location of voltage sag source by using artificial neural network
title_full Location of voltage sag source by using artificial neural network
title_fullStr Location of voltage sag source by using artificial neural network
title_full_unstemmed Location of voltage sag source by using artificial neural network
title_sort location of voltage sag source by using artificial neural network
publisher Penerbit UTM Press
publishDate 2017
url http://eprints.uthm.edu.my/7201/1/J14061_80e46e38c44d85d6637a8b387dbecb35.pdf
http://eprints.uthm.edu.my/7201/
_version_ 1738581589471789056
score 13.211869

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