| Texto completo | |
| Autor(es): |
Tominaga, Rafael N.
;
Rocha, Rodolfo V.
;
Avila, Sergio L.
;
Monaro, Renato M.
;
Salles, Mauricio B. C.
;
Carmo, Bruno S.
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2025 IEEE INTERNATIONAL ELECTRIC MACHINES & DRIVES CONFERENCE, IEMDC; v. N/A, p. 3-pg., 2025-01-01. |
| Resumo | |
One of the primary drawbacks of neural networks is that, despite usually offering superior accuracy compared to classical machine learning methods, their interpretability is often limited or even non-existent. It is easier to understand the decision-making process in support vector machine and k-nearest neighbors, for example. To contribute so that knowledge about the problem to be solved is directly incorporated into the method that seeks solutions, we propose this study. A neural network model based on decision trees is proposed to improve the prediction of a recurrent issue in power systems specifically designed to detect and classify electrical internal generator faults. Unlike conventional methods, the proposed model offers distinct advantages in terms of modularity, versatility, and interpretability, making it particularly suitable for specific applications. Validation results support these benefits, showing no evidence of a decline in prediction accuracy because of its tree-based structure. (AU) | |
| Processo FAPESP: | 20/15230-5 - Centro de Pesquisa e Inovação de Gases de Efeito Estufa - RCG2I |
| Beneficiário: | Julio Romano Meneghini |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada |