Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
This work employs machine learning methods to develop and test a technique for dynamic stability analysis of the mathematical model of a power system. A distinctive feature of the proposed method is the absence of a priori parameters of the power system model. Thus, the adaptability of the dynamic s...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/32530 |
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| _version_ | 1864513437139206144 |
|---|---|
| author | Senyuk, Mihail |
| author2 | Safaraliev, Murodbek Kamalov, Firuz Sulieman, Hana |
| author2_role | author author author |
| author_facet | Senyuk, Mihail Safaraliev, Murodbek Kamalov, Firuz Sulieman, Hana |
| author_role | author |
| dc.creator.none.fl_str_mv | Senyuk, Mihail Safaraliev, Murodbek Kamalov, Firuz Sulieman, Hana |
| dc.date.none.fl_str_mv | 2023 2025-12-08T07:12:13Z 2025-12-08T07:12:13Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Senyuk, M.; Safaraliev, M.; Kamalov, F.; Sulieman, H. Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology. Mathematics 2023, 11, 525. https://doi.org/10.3390/math11030525 2227-7390 https://hdl.handle.net/11073/32530 10.3390/math11030525 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | https://doi.org/10.3390/math11030525 |
| dc.subject.none.fl_str_mv | Ensemble machine learning Extreme gradient boosting Power system modeling Random forest Transient stability |
| dc.title.none.fl_str_mv | Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This work employs machine learning methods to develop and test a technique for dynamic stability analysis of the mathematical model of a power system. A distinctive feature of the proposed method is the absence of a priori parameters of the power system model. Thus, the adaptability of the dynamic stability assessment is achieved. The selected research topic relates to the issue of changing the structure and parameters of modern power systems. The key features of modern power systems include the following: decreased total inertia caused by integration of renewable sources energy, stricter requirements for emergency control accuracy, highly digitized operation and control of power systems, and high volumes of data that describe power system operation. Arranging emergency control in these new conditions is one of the prominent problems in modern power systems. In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. Transient stability of a power system was analyzed as the base function. Features of transmission line maintenance were used to increase accuracy of estimation. Algorithms were tested using the test power system IEEE39. In the case of the test sample, accuracy of instability classification for XGBoost was 91.5%, while that for Random Forest was 81.6%. The accuracy of algorithms increased by 10.9% and 1.5%, respectively, when the topology of the power system was taken into account. |
| format | article |
| id | aus_67c901828bd50aaa0d051fc2ac2300a8 |
| identifier_str_mv | Senyuk, M.; Safaraliev, M.; Kamalov, F.; Sulieman, H. Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology. Mathematics 2023, 11, 525. https://doi.org/10.3390/math11030525 2227-7390 10.3390/math11030525 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/32530 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | MDPI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid TopologySenyuk, MihailSafaraliev, MurodbekKamalov, FiruzSulieman, HanaEnsemble machine learningExtreme gradient boostingPower system modelingRandom forestTransient stabilityThis work employs machine learning methods to develop and test a technique for dynamic stability analysis of the mathematical model of a power system. A distinctive feature of the proposed method is the absence of a priori parameters of the power system model. Thus, the adaptability of the dynamic stability assessment is achieved. The selected research topic relates to the issue of changing the structure and parameters of modern power systems. The key features of modern power systems include the following: decreased total inertia caused by integration of renewable sources energy, stricter requirements for emergency control accuracy, highly digitized operation and control of power systems, and high volumes of data that describe power system operation. Arranging emergency control in these new conditions is one of the prominent problems in modern power systems. In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. Transient stability of a power system was analyzed as the base function. Features of transmission line maintenance were used to increase accuracy of estimation. Algorithms were tested using the test power system IEEE39. In the case of the test sample, accuracy of instability classification for XGBoost was 91.5%, while that for Random Forest was 81.6%. The accuracy of algorithms increased by 10.9% and 1.5%, respectively, when the topology of the power system was taken into account.American University of SharjahMDPI2025-12-08T07:12:13Z2025-12-08T07:12:13Z2023Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfSenyuk, M.; Safaraliev, M.; Kamalov, F.; Sulieman, H. Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology. Mathematics 2023, 11, 525. https://doi.org/10.3390/math110305252227-7390https://hdl.handle.net/11073/3253010.3390/math11030525en_UShttps://doi.org/10.3390/math11030525oai:repository.aus.edu:11073/325302025-12-08T11:18:38Z |
| spellingShingle | Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology Senyuk, Mihail Ensemble machine learning Extreme gradient boosting Power system modeling Random forest Transient stability |
| status_str | publishedVersion |
| title | Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology |
| title_full | Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology |
| title_fullStr | Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology |
| title_full_unstemmed | Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology |
| title_short | Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology |
| title_sort | Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology |
| topic | Ensemble machine learning Extreme gradient boosting Power system modeling Random forest Transient stability |
| url | https://hdl.handle.net/11073/32530 |