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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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). |
format | Online Article Text |
id | pubmed-9318332 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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