Stability improvement of the PSS-connected power system network with ensemble machine learning tool
<p>Stability is a primary requirement of the electrical power system for its flawless, secure, and economical operation. Low-frequency oscillations (LFOs), commonly seen in interconnected power systems, initiate the possibility of instability and, therefore, require sophisticated care to deal...
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2022
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| author | M.S. Shahriar (19517536) |
| author2 | M. Shafiullah (19517539) M.I.H. Pathan (19517542) Y.A. Sha’aban (19517545) Houssem R.E.H. Bouchekara (19517548) Makbul A.M. Ramli (19517551) M.M. Rahman (19517554) |
| author2_role | author author author author author author |
| author_facet | M.S. Shahriar (19517536) M. Shafiullah (19517539) M.I.H. Pathan (19517542) Y.A. Sha’aban (19517545) Houssem R.E.H. Bouchekara (19517548) Makbul A.M. Ramli (19517551) M.M. Rahman (19517554) |
| author_role | author |
| dc.creator.none.fl_str_mv | M.S. Shahriar (19517536) M. Shafiullah (19517539) M.I.H. Pathan (19517542) Y.A. Sha’aban (19517545) Houssem R.E.H. Bouchekara (19517548) Makbul A.M. Ramli (19517551) M.M. Rahman (19517554) |
| dc.date.none.fl_str_mv | 2022-11-09T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.egyr.2022.08.225 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Stability_improvement_of_the_PSS-connected_power_system_network_with_ensemble_machine_learning_tool/26889346 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Machine learning Artificial intelligence Backtracking search algorithm Ensemble method Extreme learning machinee Genetic programming Low-frequency oscillation Neurogenetic Power system stability Real-time |
| dc.title.none.fl_str_mv | Stability improvement of the PSS-connected power system network with ensemble machine learning tool |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Stability is a primary requirement of the electrical power system for its flawless, secure, and economical operation. Low-frequency oscillations (LFOs), commonly seen in interconnected power systems, initiate the possibility of instability and, therefore, require sophisticated care to deal with. This paper proposes an original approach to tuning the parameters of the power system stabilizer (PSS), which plays a crucial role in the power system networks to dampen unwanted oscillations. The ensemble method combines multiple machine learning techniques and has been used for tuning the PSS parameters in real-time for two PSS-connected power system networks. The first system is a single-machine infinite bus power system, while the second is a unified power flow controller (UPFC) device. The backtracking search algorithm (BSA) based proposed ensemble model is formed by combining three machine learning (ML) techniques, namely the extreme learning machine (ELM), neurogenetic (NG) system, and multi-gene genetic programming (MGGP). To validate the stability of the network, Eigenvalues, well-recognized statistical parameters, and minimum damping ratios were analyzed, besides the time-domain simulation results. Furthermore, results for various loading conditions were prepared to check the robustness of the proposed model. A comparative study of the proposed approach with NG, ELM, MGGP models, and two reference cases along with the conventional method will validate the superiority of the employed ML approach.</p><h2>Other Information</h2> <p> Published in: Energy Reports<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.egyr.2022.08.225" target="_blank">https://dx.doi.org/10.1016/j.egyr.2022.08.225</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4f9d1b9521c5a1c61f84f4c3fc1ecef1 |
| identifier_str_mv | 10.1016/j.egyr.2022.08.225 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26889346 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Stability improvement of the PSS-connected power system network with ensemble machine learning toolM.S. Shahriar (19517536)M. Shafiullah (19517539)M.I.H. Pathan (19517542)Y.A. Sha’aban (19517545)Houssem R.E.H. Bouchekara (19517548)Makbul A.M. Ramli (19517551)M.M. Rahman (19517554)EngineeringElectrical engineeringInformation and computing sciencesMachine learningArtificial intelligenceBacktracking search algorithmEnsemble methodExtreme learning machineeGenetic programmingLow-frequency oscillationNeurogeneticPower system stabilityReal-time<p>Stability is a primary requirement of the electrical power system for its flawless, secure, and economical operation. Low-frequency oscillations (LFOs), commonly seen in interconnected power systems, initiate the possibility of instability and, therefore, require sophisticated care to deal with. This paper proposes an original approach to tuning the parameters of the power system stabilizer (PSS), which plays a crucial role in the power system networks to dampen unwanted oscillations. The ensemble method combines multiple machine learning techniques and has been used for tuning the PSS parameters in real-time for two PSS-connected power system networks. The first system is a single-machine infinite bus power system, while the second is a unified power flow controller (UPFC) device. The backtracking search algorithm (BSA) based proposed ensemble model is formed by combining three machine learning (ML) techniques, namely the extreme learning machine (ELM), neurogenetic (NG) system, and multi-gene genetic programming (MGGP). To validate the stability of the network, Eigenvalues, well-recognized statistical parameters, and minimum damping ratios were analyzed, besides the time-domain simulation results. Furthermore, results for various loading conditions were prepared to check the robustness of the proposed model. A comparative study of the proposed approach with NG, ELM, MGGP models, and two reference cases along with the conventional method will validate the superiority of the employed ML approach.</p><h2>Other Information</h2> <p> Published in: Energy Reports<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.egyr.2022.08.225" target="_blank">https://dx.doi.org/10.1016/j.egyr.2022.08.225</a></p>2022-11-09T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.egyr.2022.08.225https://figshare.com/articles/journal_contribution/Stability_improvement_of_the_PSS-connected_power_system_network_with_ensemble_machine_learning_tool/26889346CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/268893462022-11-09T03:00:00Z |
| spellingShingle | Stability improvement of the PSS-connected power system network with ensemble machine learning tool M.S. Shahriar (19517536) Engineering Electrical engineering Information and computing sciences Machine learning Artificial intelligence Backtracking search algorithm Ensemble method Extreme learning machinee Genetic programming Low-frequency oscillation Neurogenetic Power system stability Real-time |
| status_str | publishedVersion |
| title | Stability improvement of the PSS-connected power system network with ensemble machine learning tool |
| title_full | Stability improvement of the PSS-connected power system network with ensemble machine learning tool |
| title_fullStr | Stability improvement of the PSS-connected power system network with ensemble machine learning tool |
| title_full_unstemmed | Stability improvement of the PSS-connected power system network with ensemble machine learning tool |
| title_short | Stability improvement of the PSS-connected power system network with ensemble machine learning tool |
| title_sort | Stability improvement of the PSS-connected power system network with ensemble machine learning tool |
| topic | Engineering Electrical engineering Information and computing sciences Machine learning Artificial intelligence Backtracking search algorithm Ensemble method Extreme learning machinee Genetic programming Low-frequency oscillation Neurogenetic Power system stability Real-time |