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An ensemble based data mining model for contingency analysis of power system under STLO

International Journal of Applied Power Engineering (IJAPE) · Vol. 12 No. 4 (2023) · hlm. 349–358

DOI
10.11591/ijape.v12.i4.pp349-358
ISSN
2722-2624, 2252-8792
Terbit
2023-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

In a large, interconnected power system, contingency analysis is a useful tool for pinpointing the potential consequences of post-event scenarios on the system's safety. In this work, the Newton-Raphson technique is applied to every single outage of a transmission line to compute the load flows. For the static security classification of the power system, the line voltage stability performance index (LVSI) is used. There are three levels of static security of power system namely: non-critical (the least severe), semi-critically insecure (the next lowest severe), and critical (the next highest severe). The various data mining techniques such as decision trees, bagging-based ensemble methods, and boosting-based ensemble methods were applied to assess the severity of the line under various loading and contingency conditions. Test systems based on the IEEE 30 bus system were used with the proposed machine learning classifiers. The experimental results proved that bagging based ensemble method provided better accuracy compared to the decision tree and the AdaBoost ensemble method for predicting the power system security assessment. The bagging-based ensemble method has a predictive accuracy of 85% and an AUC of 0.94.

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  • Presidency University
  • Mohan Babu University
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Rekan penulisOrganisasiArtikel bersama
Suresh KumarPresidency University1
A K VijayalakshmiAfiliasi tidak diketahui1
G. N. Vidya ShreePresidency University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijape.iaescore.com/index.php/IJAPE/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">An ensemble based data mining model for contingency analysis of power system under STLO</dc:title>
	<dc:creator>Angadi, Ravi V.</dc:creator>
	<dc:creator>Mangai, J. Alamelu</dc:creator>
	<dc:creator>Manohar, V. Joshi</dc:creator>
	<dc:creator>Daram, Suresh Babu</dc:creator>
	<dc:creator>Rao, Paritala Venkateswara</dc:creator>
	<dc:subject xml:lang="en-US">AdaBoost classifier; bagging classifier; contingency analysis; data mining; decision tree; performance indices; severity prediction</dc:subject>
	<dc:description xml:lang="en-US">In a large, interconnected power system, contingency analysis is a useful tool for pinpointing the potential consequences of post-event scenarios on the system's safety. In this work, the Newton-Raphson technique is applied to every single outage of a transmission line to compute the load flows. For the static security classification of the power system, the line voltage stability performance index (LVSI) is used. There are three levels of static security of power system namely: non-critical (the least severe), semi-critically insecure (the next lowest severe), and critical (the next highest severe). The various data mining techniques such as decision trees, bagging-based ensemble methods, and boosting-based ensemble methods were applied to assess the severity of the line under various loading and contingency conditions. Test systems based on the IEEE 30 bus system were used with the proposed machine learning classifiers. The experimental results proved that bagging based ensemble method provided better accuracy compared to the decision tree and the AdaBoost ensemble method for predicting the power system security assessment. The bagging-based ensemble method has a predictive accuracy of 85% and an AUC of 0.94.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Nil</dc:contributor>
	<dc:date>2023-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijape.iaescore.com/index.php/IJAPE/article/view/20599</dc:identifier>
	<dc:identifier>10.11591/ijape.v12.i4.pp349-358</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Applied Power Engineering (IJAPE); Vol 12, No 4: December 2023; 349-358</dc:source>
	<dc:source>2722-2624</dc:source>
	<dc:source>2252-8792</dc:source>
	<dc:source>10.11591/ijape.v12.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijape.iaescore.com/index.php/IJAPE/article/view/20599/13151</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Ravi V Angadi</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>