Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers
This paper proposed a novel algorithm based on advanced feature selection technique for decision tree (DT) classifier to assess the dynamic security in power system. The proposed methodology utilized symmetrical uncertainty (SU) to reduce the data redundancy in a dataset for DT classifier based dyna...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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The Scientific and Technological Research Council of Turkey
2017
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| Online Access: | http://eprints.uthm.edu.my/3958/1/AJ%202017%20%28546%29.pdf |
| _version_ | 1825637174695952384 |
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| author | Al-Gubri, Qusay Mohd Ariff, Mohd Aifaa |
| author_facet | Al-Gubri, Qusay Mohd Ariff, Mohd Aifaa |
| author_sort | Al-Gubri, Qusay |
| collection | UTHM |
| description | This paper proposed a novel algorithm based on advanced feature selection technique for decision tree (DT) classifier to assess the dynamic security in power system. The proposed methodology utilized symmetrical uncertainty (SU) to reduce the data redundancy in a dataset for DT classifier based dynamic security assessment (DSA) tools. The results show that SU reduces the dimension of the dataset used for DSA significantly. Subsequently, the approach improves the performance of DT classifier. The effectiveness of the proposed technique is demonstrated on modified IEEE 30-bus test system model. The results show that the DT classifier with SU outperform the DT classifier without SU. The performance of the proposed algorithm indicates that the DT classifier with SU is able to assess the dynamic security of the system in near real-time. Therefore, it is able to provide vital information for protection and control application in power system operation. |
| first_indexed | 2024-03-05T21:47:13Z |
| format | Article |
| id | uthm.eprints-3958 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:47:13Z |
| publishDate | 2017 |
| publisher | The Scientific and Technological Research Council of Turkey |
| record_format | dspace |
| spelling | uthm.eprints-39582021-11-22T07:48:18Z http://eprints.uthm.edu.my/3958/ Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers Al-Gubri, Qusay Mohd Ariff, Mohd Aifaa TK1001-1841 Production of electric energy or power. Powerplants. Central stations This paper proposed a novel algorithm based on advanced feature selection technique for decision tree (DT) classifier to assess the dynamic security in power system. The proposed methodology utilized symmetrical uncertainty (SU) to reduce the data redundancy in a dataset for DT classifier based dynamic security assessment (DSA) tools. The results show that SU reduces the dimension of the dataset used for DSA significantly. Subsequently, the approach improves the performance of DT classifier. The effectiveness of the proposed technique is demonstrated on modified IEEE 30-bus test system model. The results show that the DT classifier with SU outperform the DT classifier without SU. The performance of the proposed algorithm indicates that the DT classifier with SU is able to assess the dynamic security of the system in near real-time. Therefore, it is able to provide vital information for protection and control application in power system operation. The Scientific and Technological Research Council of Turkey 2017 Article PeerReviewed text en http://eprints.uthm.edu.my/3958/1/AJ%202017%20%28546%29.pdf Al-Gubri, Qusay and Mohd Ariff, Mohd Aifaa (2017) Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers. Turkish Journal of Electrical Engineering and Computer Sciences, 26 (NIL). pp. 2104-2116. ISSN 1300-0632 https://dx.doi.org/10.3906/elk-1705-268 |
| spellingShingle | TK1001-1841 Production of electric energy or power. Powerplants. Central stations Al-Gubri, Qusay Mohd Ariff, Mohd Aifaa Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers |
| title | Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers |
| title_full | Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers |
| title_fullStr | Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers |
| title_full_unstemmed | Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers |
| title_short | Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers |
| title_sort | real time power system dynamic security assessment based on advanced feature selection for decision tree classifiers |
| topic | TK1001-1841 Production of electric energy or power. Powerplants. Central stations |
| url | http://eprints.uthm.edu.my/3958/1/AJ%202017%20%28546%29.pdf |
| work_keys_str_mv | AT algubriqusay realtimepowersystemdynamicsecurityassessmentbasedonadvancedfeatureselectionfordecisiontreeclassifiers AT mohdariffmohdaifaa realtimepowersystemdynamicsecurityassessmentbasedonadvancedfeatureselectionfordecisiontreeclassifiers |