| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Sao Paulo, Escola Engn Sao Carlos, Dept Engn Eletr, Av Trabalhador SaoCarlense 400, BR-13560970 Sao Carlos, SP - Brazil
[2] Sao Paulo State Univ, Unesp Univ Estadual Paulista, Inst Biociencias Letras & Ciencias Exatas, Rua Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | MEASUREMENT; v. 170, JAN 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Energy quality, either in centralized or distributed generation, is directly affected by events in electrical lines. Consequently, the precise identification of those issues is of paramount importance, where the features extracted from domestic or industrial low-voltage sources should be able to properly represent the events for a subsequent classification. Nevertheless, current algorithms for event diagnosis suffer from a number of drawbacks such as the lack of real data to model the problem, since the majority of strategies is supported by simulated signals, and the uncertainty on the best features to conveniently address the occurrences. Thus, our contribution in this paper is twofold: we describe our own database, which is freely available under request, and innovatively apply Paraconsistent Feature Engineering (PFE) to analyze and select favorite wavelet-based features to classify events in low-voltage grids. Lastly, an example application where a set of features was capable of distinguishing specific events from normal signals with a value of accuracy of 96% using just an Euclidean distance classifier is shown, reassuring the efficacy of the proposed approach. Notably, the association of wavelets with PFE to handle energy quality issues had never been reported in literature. (AU) | |
| FAPESP's process: | 19/04475-0 - Paraconsistent Feature Analysis of Speech Signals: fighting the voice spoofing attacks |
| Grantee: | Rodrigo Capobianco Guido |
| Support Opportunities: | Regular Research Grants |