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Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy
Aiming at the fact that the independent component analysis algorithm requires more measurement points and cannot solve the problem of harmonic source location under underdetermined conditions, a new method based on sparse component analysis and minimum conditional entropy for identifying multiple ha...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516497/ https://www.ncbi.nlm.nih.gov/pubmed/33285840 http://dx.doi.org/10.3390/e22010065 |
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author | Du, Yongzhen Yang, Honggeng Ma, Xiaoyang |
author_facet | Du, Yongzhen Yang, Honggeng Ma, Xiaoyang |
author_sort | Du, Yongzhen |
collection | PubMed |
description | Aiming at the fact that the independent component analysis algorithm requires more measurement points and cannot solve the problem of harmonic source location under underdetermined conditions, a new method based on sparse component analysis and minimum conditional entropy for identifying multiple harmonic source locations in a distribution system is proposed. Under the condition that the network impedance is unknown and the number of harmonic sources is undetermined, the measurement node configuration algorithm selects the node position to make the separated harmonic current more accurate. Then, using the harmonic voltage data of the selected node as the input, the sparse component analysis is used to solve the harmonic current waveform under underdetermination. Finally, the conditional entropy between the harmonic current and the system node is calculated, and the node corresponding to the minimum condition entropy is the location of the harmonic source. In order to verify the effectiveness and accuracy of the proposed method, the simulation was performed in an IEEE 14-node system. Moreover, compared with the results of independent component analysis algorithms. Simulation results verify the correctness and effectiveness of the proposed algorithm. |
format | Online Article Text |
id | pubmed-7516497 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75164972020-11-09 Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy Du, Yongzhen Yang, Honggeng Ma, Xiaoyang Entropy (Basel) Article Aiming at the fact that the independent component analysis algorithm requires more measurement points and cannot solve the problem of harmonic source location under underdetermined conditions, a new method based on sparse component analysis and minimum conditional entropy for identifying multiple harmonic source locations in a distribution system is proposed. Under the condition that the network impedance is unknown and the number of harmonic sources is undetermined, the measurement node configuration algorithm selects the node position to make the separated harmonic current more accurate. Then, using the harmonic voltage data of the selected node as the input, the sparse component analysis is used to solve the harmonic current waveform under underdetermination. Finally, the conditional entropy between the harmonic current and the system node is calculated, and the node corresponding to the minimum condition entropy is the location of the harmonic source. In order to verify the effectiveness and accuracy of the proposed method, the simulation was performed in an IEEE 14-node system. Moreover, compared with the results of independent component analysis algorithms. Simulation results verify the correctness and effectiveness of the proposed algorithm. MDPI 2020-01-03 /pmc/articles/PMC7516497/ /pubmed/33285840 http://dx.doi.org/10.3390/e22010065 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Du, Yongzhen Yang, Honggeng Ma, Xiaoyang Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy |
title | Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy |
title_full | Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy |
title_fullStr | Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy |
title_full_unstemmed | Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy |
title_short | Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy |
title_sort | multi-harmonic source localization based on sparse component analysis and minimum conditional entropy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516497/ https://www.ncbi.nlm.nih.gov/pubmed/33285840 http://dx.doi.org/10.3390/e22010065 |
work_keys_str_mv | AT duyongzhen multiharmonicsourcelocalizationbasedonsparsecomponentanalysisandminimumconditionalentropy AT yanghonggeng multiharmonicsourcelocalizationbasedonsparsecomponentanalysisandminimumconditionalentropy AT maxiaoyang multiharmonicsourcelocalizationbasedonsparsecomponentanalysisandminimumconditionalentropy |