Wavelet-fuzzy power quality diagnosis system with inference method based on overlap functions: case study in an AC microgrid

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dc.contributor.authorNolasco, Diego H. S.
dc.contributor.authorCosta, Flávio Bezerra
dc.contributor.authorPalmeira, Eduardo Silva
dc.contributor.authorAlves, Denis Keuton
dc.contributor.authorBedregal, Benjamín Rene Callejas
dc.contributor.authorRocha, Thiago de Oliveira Alves
dc.contributor.authorRibeiro, Ricardo Lúcio de Araújo
dc.contributor.authorSilva, Juliano Costa Leal da
dc.date.accessioned2020-08-13T00:27:49Z
dc.date.available2020-08-13T00:27:49Z
dc.date.issued2019-05-30
dc.description.resumoThis work proposes a wavelet-fuzzy power quality (PQ) diagnosis method able to evaluate the PQ impact of steady-state (stationary) PQ events in alternating current (AC) microgrids considering the influence of the power level penetration. The proposed method is composed by a wavelet packet-based signal processing to compute the root mean square (RMS) and steady-state PQ indices of measured voltages and currents, providing accurate results even if transient disturbances take place. Thereafter, a cascade-type hierarchical fuzzy system receives the PQ indices and performs the power quality diagnosis to evaluate the impacts of disturbances on electrical system power quality. The proposed method considers subjectivities of several PQ standards simultaneously and applies an adaptive algorithm that allows the evaluation of the PQ diagnosis from the total harmonic distortion of currents considering different levels of power penetration of microgrids. Experimental results obtained from an ac microgrid laboratory setup evaluates the proposed PQ diagnosis method. In addition, the fuzzy system uses a new inference concept based on an extended n-dimensional overlap functionpt_BR
dc.identifier.citationNOLASCO, D.H.S.; COSTA, F.B.; PALMEIRA, E.S.; ALVES, D.K.; BEDREGAL, B.R.C.; ROCHA, T.O.A.; RIBEIRO, R.L.A.; SILVA, J.C.L.. Wavelet-fuzzy power quality diagnosis system with inference method based on overlap functions: case study in an ac microgrid. Engineering Applications of Artificial Intelligence, [s.l.], v. 85, p. 284-294, out. 2019. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S0952197619301241?via%3Dihub#!. Acesso em: 10 ago. 2020. http://dx.doi.org/10.1016/j.engappai.2019.05.016pt_BR
dc.identifier.doi10.1016/j.engappai.2019.05.016
dc.identifier.issn0952-1976
dc.identifier.urihttps://repositorio.ufrn.br/jspui/handle/123456789/29808
dc.languageenpt_BR
dc.publisherElsevierpt_BR
dc.subjectPower qualitypt_BR
dc.subjectMicrogridpt_BR
dc.subjectWavelet packetpt_BR
dc.subjectHierarchical fuzzy systempt_BR
dc.subjectExtended overlap functionpt_BR
dc.titleWavelet-fuzzy power quality diagnosis system with inference method based on overlap functions: case study in an AC microgridpt_BR
dc.typearticlept_BR

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