Static power system security assessment via artificial neural network.
Maintaining system security is an important factor in the operation of a power system. The aim of this study is to evaluate the reliability using artificial neural network (ANN) in static security assessment to determine the security status of a power system. Feed Forward Back Propagation Neural N...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/23187/ http://psasir.upm.edu.my/id/eprint/23187/1/Static%20power%20system%20security%20assessment%20via%20artificial%20neural%20network.pdf |
| _version_ | 1848844686919204864 |
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| author | Jasni, Jasronita Ab Kadir, Mohd Zainal Abidin |
| author_facet | Jasni, Jasronita Ab Kadir, Mohd Zainal Abidin |
| author_sort | Jasni, Jasronita |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Maintaining system security is an important factor in the operation of a power system. The aim of this
study is to evaluate the reliability using artificial neural network (ANN) in static security assessment to
determine the security status of a power system. Feed Forward Back Propagation Neural Network is
implemented to classify the security condition of IEEE 9 bus system. The input data of ANN are derived
from offline Newton Raphson load flow analysis. The result obtained from the ANN method is compared
with the Newton Raphson load flow analysis in terms of accuracy to predict the security level of IEEE 9
bus system and the computational time required by each method. The average time required by Newton-
Raphson load flow analysis to evaluate security level of IEEE 9 bus system is 0.0481 seconds while the
average time required by neural network is 0.0119 seconds. The accuracy of 13 hidden neurons feed
forward back propagation neural network to predict the security level of IEEE 9 bus system is 98.57%. In
conclusion, ANN is found to be reliable to evaluate the security level of IEEE 9 bus system. |
| first_indexed | 2025-11-15T08:34:52Z |
| format | Article |
| id | upm-23187 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:34:52Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-231872015-10-07T03:16:54Z http://psasir.upm.edu.my/id/eprint/23187/ Static power system security assessment via artificial neural network. Jasni, Jasronita Ab Kadir, Mohd Zainal Abidin Maintaining system security is an important factor in the operation of a power system. The aim of this study is to evaluate the reliability using artificial neural network (ANN) in static security assessment to determine the security status of a power system. Feed Forward Back Propagation Neural Network is implemented to classify the security condition of IEEE 9 bus system. The input data of ANN are derived from offline Newton Raphson load flow analysis. The result obtained from the ANN method is compared with the Newton Raphson load flow analysis in terms of accuracy to predict the security level of IEEE 9 bus system and the computational time required by each method. The average time required by Newton- Raphson load flow analysis to evaluate security level of IEEE 9 bus system is 0.0481 seconds while the average time required by neural network is 0.0119 seconds. The accuracy of 13 hidden neurons feed forward back propagation neural network to predict the security level of IEEE 9 bus system is 98.57%. In conclusion, ANN is found to be reliable to evaluate the security level of IEEE 9 bus system. 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/23187/1/Static%20power%20system%20security%20assessment%20via%20artificial%20neural%20network.pdf Jasni, Jasronita and Ab Kadir, Mohd Zainal Abidin (2011) Static power system security assessment via artificial neural network. Journal of Theoretical and Applied Information Technology, 31 (2). pp. 119-128. ISSN 1992-8645 http://www.jatit.org/volumes/Vol31No2/thirtyfirst_volume_2_2011.php |
| spellingShingle | Jasni, Jasronita Ab Kadir, Mohd Zainal Abidin Static power system security assessment via artificial neural network. |
| title | Static power system security assessment via artificial neural network. |
| title_full | Static power system security assessment via artificial neural network. |
| title_fullStr | Static power system security assessment via artificial neural network. |
| title_full_unstemmed | Static power system security assessment via artificial neural network. |
| title_short | Static power system security assessment via artificial neural network. |
| title_sort | static power system security assessment via artificial neural network. |
| url | http://psasir.upm.edu.my/id/eprint/23187/ http://psasir.upm.edu.my/id/eprint/23187/ http://psasir.upm.edu.my/id/eprint/23187/1/Static%20power%20system%20security%20assessment%20via%20artificial%20neural%20network.pdf |