Evaluation of conductor rupture classification metrics using clustering and supervised algorithms.
In: Electric Power Systems Research, Jg. 233 (2024-08-01), S. N.PAG
academicJournal
Zugriff:
Conductor Rupture (CR) is a critical problem in Distribution Systems (DSs), as it is challenging to identify and distinguish it from other events, which becomes even more complex when considering systems with Distributed Generation (DG). Accordingly, this short communication aims to analyze new metrics based on voltage and current negative sequence components in a DS with DG to differentiate CR from other events. The analysis comprises noisy and non-noisy signals, different statistics, and rupture locations using clustering and supervised algorithms. Overall, this study can help researchers develop increasingly effective CR detection methods, acting before the cables touch the ground, avoiding damage to living beings and bushfires. • Use of clustering and supervised algorithms to detect conductor rupture. • Exploring new metrics derived from voltage and current negative sequence components. • Analysis considering the presence of distributed generation and noisy signals. • Evaluation of the ability to distinguish conductor rupture from other events. [ABSTRACT FROM AUTHOR]
Titel: |
Evaluation of conductor rupture classification metrics using clustering and supervised algorithms.
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Autor/in / Beteiligte Person: | Lopes, Gabriela Nunes ; da Silva, Maurício Pavani ; Vieira, José Carlos M. |
Zeitschrift: | Electric Power Systems Research, Jg. 233 (2024-08-01), S. N.PAG |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 0378-7796 (print) |
DOI: | 10.1016/j.epsr.2024.110531 |
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