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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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2017
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| Online Access: | http://eprints.uthm.edu.my/7201/1/J14061_80e46e38c44d85d6637a8b387dbecb35.pdf |
| _version_ | 1825710334025924608 |
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| author | Wagiman, Khairul Rijal H. Shareef, S. N. Khalidb |
| author_facet | Wagiman, Khairul Rijal H. Shareef, S. N. Khalidb |
| author_sort | Wagiman, Khairul Rijal |
| collection | UTHM |
| 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. |
| first_indexed | 2024-03-05T21:56:01Z |
| format | Article |
| id | uthm.eprints-7201 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:56:01Z |
| publishDate | 2017 |
| publisher | Penerbit UTM Press |
| record_format | dspace |
| spelling | 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. |
| spellingShingle | T Technology (General) Wagiman, Khairul Rijal H. Shareef, S. N. Khalidb Location of voltage sag source by using artificial neural network |
| title | 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_short | Location of voltage sag source by using artificial neural network |
| title_sort | location of voltage sag source by using artificial neural network |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/7201/1/J14061_80e46e38c44d85d6637a8b387dbecb35.pdf |
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