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A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation

In order to overcome the spectral interference of the conventional Fourier transform in the International Electrotechnical Commission framework, this paper introduces a Bregman-split-based compressive sensing (BSCS) method to estimate the Taylor–Fourier coefficients in a multi-frequency dynamic phas...

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Detalles Bibliográficos
Autores principales: Chi, Aobing, Zeng, Chengbi, Guo, Yufu, Miao, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318332/
https://www.ncbi.nlm.nih.gov/pubmed/35885211
http://dx.doi.org/10.3390/e24070988
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author Chi, Aobing
Zeng, Chengbi
Guo, Yufu
Miao, Hong
author_facet Chi, Aobing
Zeng, Chengbi
Guo, Yufu
Miao, Hong
author_sort Chi, Aobing
collection PubMed
description In order to overcome the spectral interference of the conventional Fourier transform in the International Electrotechnical Commission framework, this paper introduces a Bregman-split-based compressive sensing (BSCS) method to estimate the Taylor–Fourier coefficients in a multi-frequency dynamic phasor model. Considering the DDC component estimation, this paper transforms the phasor problem into a compressive sensing model based on the regularity and sparsity of the dynamic harmonic signal distribution. It then derives an optimized hybrid regularization algorithm with the Bregman split method to reconstruct the dynamic phasor estimation. The accuracy of the model was verified by using the cross entropy to measure the distribution differences of values. Composite tests derived from the dynamic phasor test conditions were then used to verify the potentialities of the BSCS method. Simulation results show that the algorithm can alleviate the impact of dynamic signals on phasor estimation and significantly improve the estimation accuracy, which provides a theoretical basis for P-class phasor measurement units (PMUs).
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spelling pubmed-93183322022-07-27 A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation Chi, Aobing Zeng, Chengbi Guo, Yufu Miao, Hong Entropy (Basel) Article In order to overcome the spectral interference of the conventional Fourier transform in the International Electrotechnical Commission framework, this paper introduces a Bregman-split-based compressive sensing (BSCS) method to estimate the Taylor–Fourier coefficients in a multi-frequency dynamic phasor model. Considering the DDC component estimation, this paper transforms the phasor problem into a compressive sensing model based on the regularity and sparsity of the dynamic harmonic signal distribution. It then derives an optimized hybrid regularization algorithm with the Bregman split method to reconstruct the dynamic phasor estimation. The accuracy of the model was verified by using the cross entropy to measure the distribution differences of values. Composite tests derived from the dynamic phasor test conditions were then used to verify the potentialities of the BSCS method. Simulation results show that the algorithm can alleviate the impact of dynamic signals on phasor estimation and significantly improve the estimation accuracy, which provides a theoretical basis for P-class phasor measurement units (PMUs). MDPI 2022-07-17 /pmc/articles/PMC9318332/ /pubmed/35885211 http://dx.doi.org/10.3390/e24070988 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chi, Aobing
Zeng, Chengbi
Guo, Yufu
Miao, Hong
A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation
title A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation
title_full A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation
title_fullStr A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation
title_full_unstemmed A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation
title_short A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation
title_sort bregman-split-based compressive sensing method for dynamic harmonic estimation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318332/
https://www.ncbi.nlm.nih.gov/pubmed/35885211
http://dx.doi.org/10.3390/e24070988
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