Improving Electrical Fault Detection Using Multiple Classifier Systems

José Oliveira, Dioeliton Passos, Davi Carvalho, José F. V. Melo, Eraylson G. Silva () and Paulo S. G. de Mattos Neto ()
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José Oliveira: Centro de Informática, Universidade Federal de Pernambuco (CIn/UFPE), Recife 50740-560, Brazil
Dioeliton Passos: Centro de Informática, Universidade Federal de Pernambuco (CIn/UFPE), Recife 50740-560, Brazil
Davi Carvalho: Centro de Informática, Universidade Federal de Pernambuco (CIn/UFPE), Recife 50740-560, Brazil
José F. V. Melo: Centro de Informática, Universidade Federal de Pernambuco (CIn/UFPE), Recife 50740-560, Brazil
Eraylson G. Silva: Campus Garanhuns—Universidade de Pernambuco, Garanhuns 55294-902, Brazil
Paulo S. G. de Mattos Neto: Centro de Informática, Universidade Federal de Pernambuco (CIn/UFPE), Recife 50740-560, Brazil

Energies, 2024, vol. 17, issue 22, 1-26

Abstract: Machine Learning-based fault detection approaches in energy systems have gained prominence for their superior performance. These automated approaches can assist operators by highlighting anomalies and faults, providing a robust framework for improving Situation Awareness. However, existing approaches predominantly rely on monolithic models, which struggle with adapting to changing data, handling imbalanced datasets, and capturing patterns in noisy environments. To overcome these challenges, this study explores the potential of Multiple Classifier System (MCS) approaches. The results demonstrate that ensemble methods generally outperform single models, with dynamic approaches like META-DES showing remarkable resilience to noise. These findings highlight the importance of model diversity and ensemble strategies in improving fault classification accuracy under real-world, noisy conditions. This research emphasizes the potential of MCS techniques as a robust solution for enhancing the reliability of fault detection systems.

Keywords: electrical transmission systems; situation awareness; fault detection; multiple classifier systems; ensemble; dynamic classifier selection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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