Wavelet-based features selected with Paraconsistent Feature Engineering successfully classify events in low-voltage grids

Autor(es)

Caobianco, Luiz Gustavo ^[1] ; Guido, Rodrigo Capobianco ^[2] ; da Silva, Ivan Nunes ^[1]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: MEASUREMENT; v. 170, JAN 2021.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 19/04475-0 - Análise Paraconsistente de Características dos Sinais de Fala: combatendo os ataques de voice spoofing
• Beneficiário: Rodrigo Capobianco Guido
• Modalidade de apoio: Auxílio à Pesquisa - Regular