Control action based on steady-state security assessment using an artificial neural network
In this paper, the application of an Artificial Neural Network (ANN) for remedial action of a power system is presented. The aims of this study are to find the significant control action that alleviates a bus voltage violation of a power system and to demonstrate the ability of a neural network in t...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2010
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| Online Access: | http://psasir.upm.edu.my/id/eprint/68936/1/Control%20action%20based%20on%20steady-state%20security%20assessment%20using%20an%20artificial%20neural%20network.pdf |
| _version_ | 1796978835311296512 |
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| author | Al-Masri, Ahmed Naufal A. Ab Kadir, Mohd Zainal Abidin Hizam, Hashim Mariun, Norman Yusof, Sallehhudin |
| author_facet | Al-Masri, Ahmed Naufal A. Ab Kadir, Mohd Zainal Abidin Hizam, Hashim Mariun, Norman Yusof, Sallehhudin |
| author_sort | Al-Masri, Ahmed Naufal A. |
| collection | UPM |
| description | In this paper, the application of an Artificial Neural Network (ANN) for remedial action of a power system is presented. The aims of this study are to find the significant control action that alleviates a bus voltage violation of a power system and to demonstrate the ability of a neural network in terms of evaluating the generation re-dispatch and load shedding amounts. The remedial action is based on a steady-state security assessment of the power system. The proposed algorithm has been successfully tested on a 9-bus test system. The results are compared with other conventional methods and it reveals that an ANN can provide the required amount of generation re-dispatch and load shedding accurately and instantaneously compared to other methods. On average, remedial actions were shown to have a positive effect for reducing the number of bus voltage violations and improving system security. |
| first_indexed | 2024-03-06T10:00:13Z |
| format | Conference or Workshop Item |
| id | upm.eprints-68936 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:00:13Z |
| publishDate | 2010 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-689362019-06-12T02:06:34Z http://psasir.upm.edu.my/id/eprint/68936/ Control action based on steady-state security assessment using an artificial neural network Al-Masri, Ahmed Naufal A. Ab Kadir, Mohd Zainal Abidin Hizam, Hashim Mariun, Norman Yusof, Sallehhudin In this paper, the application of an Artificial Neural Network (ANN) for remedial action of a power system is presented. The aims of this study are to find the significant control action that alleviates a bus voltage violation of a power system and to demonstrate the ability of a neural network in terms of evaluating the generation re-dispatch and load shedding amounts. The remedial action is based on a steady-state security assessment of the power system. The proposed algorithm has been successfully tested on a 9-bus test system. The results are compared with other conventional methods and it reveals that an ANN can provide the required amount of generation re-dispatch and load shedding accurately and instantaneously compared to other methods. On average, remedial actions were shown to have a positive effect for reducing the number of bus voltage violations and improving system security. IEEE 2010 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68936/1/Control%20action%20based%20on%20steady-state%20security%20assessment%20using%20an%20artificial%20neural%20network.pdf Al-Masri, Ahmed Naufal A. and Ab Kadir, Mohd Zainal Abidin and Hizam, Hashim and Mariun, Norman and Yusof, Sallehhudin (2010) Control action based on steady-state security assessment using an artificial neural network. In: 2010 IEEE International Conference on Power and Energy (PECon 2010), 29 Nov.-1 Dec. 2010, Kuala Lumpur, Malaysia. (pp. 706-711). 10.1109/PECON.2010.5697671 |
| spellingShingle | Al-Masri, Ahmed Naufal A. Ab Kadir, Mohd Zainal Abidin Hizam, Hashim Mariun, Norman Yusof, Sallehhudin Control action based on steady-state security assessment using an artificial neural network |
| title | Control action based on steady-state security assessment using an artificial neural network |
| title_full | Control action based on steady-state security assessment using an artificial neural network |
| title_fullStr | Control action based on steady-state security assessment using an artificial neural network |
| title_full_unstemmed | Control action based on steady-state security assessment using an artificial neural network |
| title_short | Control action based on steady-state security assessment using an artificial neural network |
| title_sort | control action based on steady state security assessment using an artificial neural network |
| url | http://psasir.upm.edu.my/id/eprint/68936/1/Control%20action%20based%20on%20steady-state%20security%20assessment%20using%20an%20artificial%20neural%20network.pdf |
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