Ensemble methods of classification for power systems security assessment
One of the most promising approaches for complex technical systems analysis employs ensemble methods of classification. Ensemble methods enable a reliable decision rules construction for feature space classification in the presence of many possible states of the system. In this paper the novel techn...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Emerald Publishing
2019-01-01
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| Series: | Applied Computing and Informatics |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2210832717300273 |
| _version_ | 1797728242270994432 |
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| author | A. Zhukov N. Tomin V. Kurbatsky D. Sidorov D. Panasetsky A. Foley |
| author_facet | A. Zhukov N. Tomin V. Kurbatsky D. Sidorov D. Panasetsky A. Foley |
| author_sort | A. Zhukov |
| collection | DOAJ |
| description | One of the most promising approaches for complex technical systems analysis employs ensemble methods of classification. Ensemble methods enable a reliable decision rules construction for feature space classification in the presence of many possible states of the system. In this paper the novel techniques based on decision trees are used to evaluate power system reliability. In this work a hybrid approach based on random forests models and boosting model is proposed. Such techniques can be applied to predict the interaction of increasing renewable power, storage devices and intelligent switching of smart loads from intelligent domestic appliances, storage heaters and air-conditioning units and electric vehicles with grid to enhance decision making. This ensemble classification method was tested on the modified 118-bus IEEE power system to examine whether the power system is secured under steady-state operating conditions. Keywords: Power system, Ensemble methods, Boosting, Classification, Heuristics, Random forests, Security assessment, 2010 MSC: 90C59, 68T05 |
| first_indexed | 2024-03-12T11:11:13Z |
| format | Article |
| id | doaj.art-fec1ca2dd5df4fb89ad9f3ca0253c035 |
| institution | Directory Open Access Journal |
| issn | 2210-8327 |
| language | English |
| last_indexed | 2024-03-12T11:11:13Z |
| publishDate | 2019-01-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Applied Computing and Informatics |
| spelling | doaj.art-fec1ca2dd5df4fb89ad9f3ca0253c0352023-09-02T03:00:59ZengEmerald PublishingApplied Computing and Informatics2210-83272019-01-011514553Ensemble methods of classification for power systems security assessmentA. Zhukov0N. Tomin1V. Kurbatsky2D. Sidorov3D. Panasetsky4A. Foley5Energy Systems Institute of Russian Academy of Sciences, Irkutsk, RussiaEnergy Systems Institute of Russian Academy of Sciences, Irkutsk, RussiaEnergy Systems Institute of Russian Academy of Sciences, Irkutsk, RussiaEnergy Systems Institute of Russian Academy of Sciences, Irkutsk, Russia; Corresponding author.Energy Systems Institute of Russian Academy of Sciences, Irkutsk, RussiaQueen’s University Belfast, Belfast, United KingdomOne of the most promising approaches for complex technical systems analysis employs ensemble methods of classification. Ensemble methods enable a reliable decision rules construction for feature space classification in the presence of many possible states of the system. In this paper the novel techniques based on decision trees are used to evaluate power system reliability. In this work a hybrid approach based on random forests models and boosting model is proposed. Such techniques can be applied to predict the interaction of increasing renewable power, storage devices and intelligent switching of smart loads from intelligent domestic appliances, storage heaters and air-conditioning units and electric vehicles with grid to enhance decision making. This ensemble classification method was tested on the modified 118-bus IEEE power system to examine whether the power system is secured under steady-state operating conditions. Keywords: Power system, Ensemble methods, Boosting, Classification, Heuristics, Random forests, Security assessment, 2010 MSC: 90C59, 68T05http://www.sciencedirect.com/science/article/pii/S2210832717300273 |
| spellingShingle | A. Zhukov N. Tomin V. Kurbatsky D. Sidorov D. Panasetsky A. Foley Ensemble methods of classification for power systems security assessment Applied Computing and Informatics |
| title | Ensemble methods of classification for power systems security assessment |
| title_full | Ensemble methods of classification for power systems security assessment |
| title_fullStr | Ensemble methods of classification for power systems security assessment |
| title_full_unstemmed | Ensemble methods of classification for power systems security assessment |
| title_short | Ensemble methods of classification for power systems security assessment |
| title_sort | ensemble methods of classification for power systems security assessment |
| url | http://www.sciencedirect.com/science/article/pii/S2210832717300273 |
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