Estimation of parameters of metal-oxide surge arrester models using Big Bang-Big Crunch and Hybrid Big Bang-Big Crunch algorithms
Metal oxide surge arrester accurate modeling and its parameter identification are very important for insulation coordination studies, arrester allocation and system reliability. Since quality and reliability of lightning performance studies can be improved with the more efficient representation of t...
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2016-07-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_578_564d25a1c25858e62346d530acbaaa91.pdf |
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| author | M.M Abravesh A Sheikholeslami H. Abravesh M. Yazdani asrami |
| author_facet | M.M Abravesh A Sheikholeslami H. Abravesh M. Yazdani asrami |
| author_sort | M.M Abravesh |
| collection | DOAJ |
| description | Metal oxide surge arrester accurate modeling and its parameter identification are very important for insulation coordination studies, arrester allocation and system reliability. Since quality and reliability of lightning performance studies can be improved with the more efficient representation of the arresters´ dynamic behavior. In this paper, Big Bang – Big Crunch and Hybrid Big Bang – Big Crunch optimization algorithms are used to selects optimum surge arrester model equivalent circuit parameters values, minimizing the error between the simulated peak residual voltage value and this given by the manufacturer.The proposed algorithms are applied to a 63 kV and 230 kV metal oxide surge arrester. The obtained results show that using this method the maximum percentage error is below 1.5 percent. |
| first_indexed | 2024-04-14T05:34:44Z |
| format | Article |
| id | doaj.art-61ea48fe06f24081a37ee22984288730 |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-04-14T05:34:44Z |
| publishDate | 2016-07-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-61ea48fe06f24081a37ee229842887302022-12-22T02:09:42ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442016-07-014223524110.5829/idosi.JAIDM.2016.04.02.12578Estimation of parameters of metal-oxide surge arrester models using Big Bang-Big Crunch and Hybrid Big Bang-Big Crunch algorithmsM.M Abravesh0A Sheikholeslami1H. Abravesh2M. Yazdani asrami3Department of Electrical Engineering, Hadaf Institute of Higher Education, Sari, IranDepartment of Electrical Engineering, Noshirvani University of Technology, Babol, IranDepartment of Electrical Engineering, Hadaf Institute of Higher Education, Sari, IranDepartment of Electrical Engineering, Noshirvani University of Technology, Babol, IranMetal oxide surge arrester accurate modeling and its parameter identification are very important for insulation coordination studies, arrester allocation and system reliability. Since quality and reliability of lightning performance studies can be improved with the more efficient representation of the arresters´ dynamic behavior. In this paper, Big Bang – Big Crunch and Hybrid Big Bang – Big Crunch optimization algorithms are used to selects optimum surge arrester model equivalent circuit parameters values, minimizing the error between the simulated peak residual voltage value and this given by the manufacturer.The proposed algorithms are applied to a 63 kV and 230 kV metal oxide surge arrester. The obtained results show that using this method the maximum percentage error is below 1.5 percent.http://jad.shahroodut.ac.ir/article_578_564d25a1c25858e62346d530acbaaa91.pdfSurge arrestersResidual voltageBig Bang – Big Crunch algorithmHybrid Big Bang – Big Crunch algorithm |
| spellingShingle | M.M Abravesh A Sheikholeslami H. Abravesh M. Yazdani asrami Estimation of parameters of metal-oxide surge arrester models using Big Bang-Big Crunch and Hybrid Big Bang-Big Crunch algorithms Journal of Artificial Intelligence and Data Mining Surge arresters Residual voltage Big Bang – Big Crunch algorithm Hybrid Big Bang – Big Crunch algorithm |
| title | Estimation of parameters of metal-oxide surge arrester models using Big Bang-Big Crunch and Hybrid Big Bang-Big Crunch algorithms |
| title_full | Estimation of parameters of metal-oxide surge arrester models using Big Bang-Big Crunch and Hybrid Big Bang-Big Crunch algorithms |
| title_fullStr | Estimation of parameters of metal-oxide surge arrester models using Big Bang-Big Crunch and Hybrid Big Bang-Big Crunch algorithms |
| title_full_unstemmed | Estimation of parameters of metal-oxide surge arrester models using Big Bang-Big Crunch and Hybrid Big Bang-Big Crunch algorithms |
| title_short | Estimation of parameters of metal-oxide surge arrester models using Big Bang-Big Crunch and Hybrid Big Bang-Big Crunch algorithms |
| title_sort | estimation of parameters of metal oxide surge arrester models using big bang big crunch and hybrid big bang big crunch algorithms |
| topic | Surge arresters Residual voltage Big Bang – Big Crunch algorithm Hybrid Big Bang – Big Crunch algorithm |
| url | http://jad.shahroodut.ac.ir/article_578_564d25a1c25858e62346d530acbaaa91.pdf |
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