Version 1
: Received: 2 October 2024 / Approved: 3 October 2024 / Online: 7 October 2024 (03:33:06 CEST)
How to cite:
Oliveira, J. W.; Passos, D.; Carvalho, D.; Melo, J. F. V.; Silva, E. G.; de Mattos Neto, P. S. G. Improving Fault Detection in Electrical Power Systems Using Multiple Classifier Systems. Preprints2024, 2024100239. https://doi.org/10.20944/preprints202410.0239.v1
Oliveira, J. W.; Passos, D.; Carvalho, D.; Melo, J. F. V.; Silva, E. G.; de Mattos Neto, P. S. G. Improving Fault Detection in Electrical Power Systems Using Multiple Classifier Systems. Preprints 2024, 2024100239. https://doi.org/10.20944/preprints202410.0239.v1
Oliveira, J. W.; Passos, D.; Carvalho, D.; Melo, J. F. V.; Silva, E. G.; de Mattos Neto, P. S. G. Improving Fault Detection in Electrical Power Systems Using Multiple Classifier Systems. Preprints2024, 2024100239. https://doi.org/10.20944/preprints202410.0239.v1
APA Style
Oliveira, J. W., Passos, D., Carvalho, D., Melo, J. F. V., Silva, E. G., & de Mattos Neto, P. S. G. (2024). Improving Fault Detection in Electrical Power Systems Using Multiple Classifier Systems. Preprints. https://doi.org/10.20944/preprints202410.0239.v1
Chicago/Turabian Style
Oliveira, J. W., Eraylson G. Silva and Paulo S. G. de Mattos Neto. 2024 "Improving Fault Detection in Electrical Power Systems Using Multiple Classifier Systems" Preprints. https://doi.org/10.20944/preprints202410.0239.v1
Abstract
The reliable operation of power systems depends on rapid fault detection to trigger protection mechanisms and prevent further damage. Machine Learning-based fault detection systems have gained prominence for their superior performance. These automated systems 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.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
