Simulation of an adaptive artificial neural network for power system security enhancement including control action
This paper presents a new method for enhancing power system security, including a remedial action, using an artificial neural network (ANN) technique. The deregulation of electricity markets is still an essential requirement of modern power systems, which require the operation of an independent syst...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/35378/ http://psasir.upm.edu.my/id/eprint/35378/1/Simulation%20of%20an%20adaptive%20artificial%20neural%20network%20for%20power%20system%20security%20enhancement%20including%20control%20action.pdf |
| _version_ | 1848848038300221440 |
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| author | Al-Masri, Ahmed Naufal A. Ab Kadir, Mohd Zainal Abidin Hizam, Hashim Mariun, Norman |
| author_facet | Al-Masri, Ahmed Naufal A. Ab Kadir, Mohd Zainal Abidin Hizam, Hashim Mariun, Norman |
| author_sort | Al-Masri, Ahmed Naufal A. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents a new method for enhancing power system security, including a remedial action, using an artificial neural network (ANN) technique. The deregulation of electricity markets is still an essential requirement of modern power systems, which require the operation of an independent system driven by economic considerations. Power flow and contingency analyses usually take a few seconds to suggest a control action. Such delay could result in issues that affect system security. This study aims to find a significant control action that alleviates the bus voltage violation of a power system and to develop an automatic data knowledge generation method for the adaptive ANN. The developed method is proved to be a steady-state security assessment tool for supplying possible control actions to mitigate an insecure situation resulting from credible contingency. The proposed algorithm is successfully tested on the IEEE 9-bus and 39-bus test systems. A comparison of the results of the proposed algorithm with those of other conventional methods reveals that an ANN can accurately and instantaneously provide the required amounts of generation re-dispatch and load shedding in megawatts. |
| first_indexed | 2025-11-15T09:28:08Z |
| format | Article |
| id | upm-35378 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:28:08Z |
| publishDate | 2015 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-353782016-06-06T07:23:16Z http://psasir.upm.edu.my/id/eprint/35378/ Simulation of an adaptive artificial neural network for power system security enhancement including control action Al-Masri, Ahmed Naufal A. Ab Kadir, Mohd Zainal Abidin Hizam, Hashim Mariun, Norman This paper presents a new method for enhancing power system security, including a remedial action, using an artificial neural network (ANN) technique. The deregulation of electricity markets is still an essential requirement of modern power systems, which require the operation of an independent system driven by economic considerations. Power flow and contingency analyses usually take a few seconds to suggest a control action. Such delay could result in issues that affect system security. This study aims to find a significant control action that alleviates the bus voltage violation of a power system and to develop an automatic data knowledge generation method for the adaptive ANN. The developed method is proved to be a steady-state security assessment tool for supplying possible control actions to mitigate an insecure situation resulting from credible contingency. The proposed algorithm is successfully tested on the IEEE 9-bus and 39-bus test systems. A comparison of the results of the proposed algorithm with those of other conventional methods reveals that an ANN can accurately and instantaneously provide the required amounts of generation re-dispatch and load shedding in megawatts. Elsevier 2015-04 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/35378/1/Simulation%20of%20an%20adaptive%20artificial%20neural%20network%20for%20power%20system%20security%20enhancement%20including%20control%20action.pdf Al-Masri, Ahmed Naufal A. and Ab Kadir, Mohd Zainal Abidin and Hizam, Hashim and Mariun, Norman (2015) Simulation of an adaptive artificial neural network for power system security enhancement including control action. Applied Soft Computing, 29. pp. 1-11. ISSN 1568-4946; ESSN: 1872-9681 http://www.sciencedirect.com/science/article/pii/S1568494614006358 10.1016/j.asoc.2014.12.006 |
| spellingShingle | Al-Masri, Ahmed Naufal A. Ab Kadir, Mohd Zainal Abidin Hizam, Hashim Mariun, Norman Simulation of an adaptive artificial neural network for power system security enhancement including control action |
| title | Simulation of an adaptive artificial neural network for power system security enhancement including control action |
| title_full | Simulation of an adaptive artificial neural network for power system security enhancement including control action |
| title_fullStr | Simulation of an adaptive artificial neural network for power system security enhancement including control action |
| title_full_unstemmed | Simulation of an adaptive artificial neural network for power system security enhancement including control action |
| title_short | Simulation of an adaptive artificial neural network for power system security enhancement including control action |
| title_sort | simulation of an adaptive artificial neural network for power system security enhancement including control action |
| url | http://psasir.upm.edu.my/id/eprint/35378/ http://psasir.upm.edu.my/id/eprint/35378/ http://psasir.upm.edu.my/id/eprint/35378/ http://psasir.upm.edu.my/id/eprint/35378/1/Simulation%20of%20an%20adaptive%20artificial%20neural%20network%20for%20power%20system%20security%20enhancement%20including%20control%20action.pdf |