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dc.creatorOliveira, Daniel Ramalho de-
dc.creatorLima, Marcelo Antonio Alves-
dc.creatorSilva, Leandro Rodrigues Manso-
dc.creatorFerreira, Danton Diego-
dc.creatorDuque, Carlos Augusto-
dc.date.accessioned2022-04-20T22:35:02Z-
dc.date.available2022-04-20T22:35:02Z-
dc.date.issued2021-02-
dc.identifier.citationOLIVEIRA, D. R. de et al. Second order blind identification algorithm with exact model order estimation for harmonic and interharmonic decomposition with reduced complexity. International Journal of Electrical Power & Energy Systems, [S. I.], v. 125, Feb. 2021. DOI: https://doi.org/10.1016/j.ijepes.2020.106415.pt_BR
dc.identifier.urihttps://doi.org/10.1016/j.ijepes.2020.106415pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/49787-
dc.description.abstractThe power distribution network is susceptible to several Power Quality (PQ) disturbances. Among those, the harmonic and interharmonic distortions should be highlighted due to their high proliferation. This work proposes the utilization of signal processing techniques to decompose the electrical voltage and/or current signals into its harmonic and interhamonic component waveforms through a Blind Source Separation (BSS) algorithm named Second Order Blind Identification (SOBI). This algorithm is normally applied to a multivariate data set, what implies in a necessity of multiple measurements in different points of the system that will be analyzed. However, Single-Channel Blind Source Separation (SCBSS) method will be proposed in this work to estimate the components via SOBI using only one measured signal point. The method works as a set of adaptive filters whose coefficients are blindly obtained via SOBI and is responsible for the components separation. An Exact Model Order (EMO) algorithm will be used to improve the performance of the SOBI algorithm in order to estimate the correct number of components to be separated. Also, the EMO will be helpful to reduce the computational complexity of the SOBI. The performance of the proposed SCBSS method will be compared to that of the SCICA (Single-Channel Independent Component Analysis) based on the well-known FastICA algorithm, which employs Higher Order Statistics (HOS). It will be shown that the proposed SCBSS overtakes the SCICA for harmonic and interharmonic decomposition in performance and in reduced computational complexity. Also, the proposed SCBSS method will be compared to EMO-ESPRIT algorithm, where will be shown that the SCBSS achieved better results in noisy and time-varying scenarios. Finally, the proposed SCBSS will be applied for the analysis of a voltage signal acquired from the simulation of a power system containing wind generation.pt_BR
dc.languageenpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceInternational Journal of Electrical Power & Energy Systemspt_BR
dc.subjectPower qualitypt_BR
dc.subjectHarmonicspt_BR
dc.subjectInterharmonicspt_BR
dc.subjectBlind source separationpt_BR
dc.subjectSecond-order statisticspt_BR
dc.titleSecond order blind identification algorithm with exact model order estimation for harmonic and interharmonic decomposition with reduced complexitypt_BR
dc.typeArtigopt_BR
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DEG - Artigos publicados em periódicos

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