Tuning of a fuzzy logic power system stabilizer using geneticalgorithms
A Hybrid Power System Stabilizer (HPSS) is presented. The proposed approach uses genetic algorithms (GA) to search for optimal or near optimal settings of fuzzy logic power system stabilizer (FLPSS) parameters. Incorporation of GA in FLPSSs design will add an intelligent dimension to these stabilize...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| التنسيق: | article |
| منشور في: |
1997
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://eprints.kfupm.edu.sa/id/eprint/14676/1/14676_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14676/2/14676_2.doc |
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| _version_ | 1864513403227209728 |
|---|---|
| author | Abido, M.A. |
| author2 | Abdel-Magid, Y.L. unknown |
| author2_role | author author |
| author_facet | Abido, M.A. Abdel-Magid, Y.L. unknown |
| author_role | author |
| dc.creator.none.fl_str_mv | Abido, M.A. Abdel-Magid, Y.L. unknown |
| dc.date.none.fl_str_mv | 1997-04 2020 |
| dc.format.none.fl_str_mv | application/pdf application/msword |
| dc.identifier.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/14676/1/14676_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14676/2/14676_2.doc (1997) Tuning of a fuzzy logic power system stabilizer using geneticalgorithms. Evolutionary Computation, 1997., IEEE International conference, 1. |
| dc.language.none.fl_str_mv | en en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/14676/ |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Computer |
| dc.title.none.fl_str_mv | Tuning of a fuzzy logic power system stabilizer using geneticalgorithms |
| dc.type.none.fl_str_mv | Article PeerReviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | A Hybrid Power System Stabilizer (HPSS) is presented. The proposed approach uses genetic algorithms (GA) to search for optimal or near optimal settings of fuzzy logic power system stabilizer (FLPSS) parameters. Incorporation of GA in FLPSSs design will add an intelligent dimension to these stabilizers and significantly reduce the time consumed in the design process. It is shown that the performance of FLPSS can be improved significantly by incorporating a genetic based learning mechanism. The performance of the proposed HPSS under different disturbances and loading conditions is investigated. The results show the superiority and robustness of the proposed HPSS as compared to classical PSS and its capability to enhance system damping over a wide range of loading conditions |
| eu_rights_str_mv | openAccess |
| format | article |
| id | KFUPM_ceaa82c0053a689042d63dcc72f7706c |
| identifier_str_mv | (1997) Tuning of a fuzzy logic power system stabilizer using geneticalgorithms. Evolutionary Computation, 1997., IEEE International conference, 1. |
| language_invalid_str_mv | en |
| network_acronym_str | KFUPM |
| network_name_str | King Fahd University of Petroleum and Minerals |
| oai_identifier_str | oai::14676 |
| publishDate | 1997 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Tuning of a fuzzy logic power system stabilizer using geneticalgorithmsAbido, M.A.Abdel-Magid, Y.L.unknownComputerA Hybrid Power System Stabilizer (HPSS) is presented. The proposed approach uses genetic algorithms (GA) to search for optimal or near optimal settings of fuzzy logic power system stabilizer (FLPSS) parameters. Incorporation of GA in FLPSSs design will add an intelligent dimension to these stabilizers and significantly reduce the time consumed in the design process. It is shown that the performance of FLPSS can be improved significantly by incorporating a genetic based learning mechanism. The performance of the proposed HPSS under different disturbances and loading conditions is investigated. The results show the superiority and robustness of the proposed HPSS as compared to classical PSS and its capability to enhance system damping over a wide range of loading conditionsIEEE1997-042020ArticlePeerReviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/mswordhttps://eprints.kfupm.edu.sa/id/eprint/14676/1/14676_1.pdfhttps://eprints.kfupm.edu.sa/id/eprint/14676/2/14676_2.doc (1997) Tuning of a fuzzy logic power system stabilizer using geneticalgorithms. Evolutionary Computation, 1997., IEEE International conference, 1. enenhttps://eprints.kfupm.edu.sa/id/eprint/14676/info:eu-repo/semantics/openAccessoai::146762019-11-01T14:06:54Z |
| spellingShingle | Tuning of a fuzzy logic power system stabilizer using geneticalgorithms Abido, M.A. Computer |
| status_str | publishedVersion |
| title | Tuning of a fuzzy logic power system stabilizer using geneticalgorithms |
| title_full | Tuning of a fuzzy logic power system stabilizer using geneticalgorithms |
| title_fullStr | Tuning of a fuzzy logic power system stabilizer using geneticalgorithms |
| title_full_unstemmed | Tuning of a fuzzy logic power system stabilizer using geneticalgorithms |
| title_short | Tuning of a fuzzy logic power system stabilizer using geneticalgorithms |
| title_sort | Tuning of a fuzzy logic power system stabilizer using geneticalgorithms |
| topic | Computer |
| url | https://eprints.kfupm.edu.sa/id/eprint/14676/1/14676_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14676/2/14676_2.doc |