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Improved empirical wavelet transform (EWT) and its application in non-stationary vibration signal of transformer

The resonant frequency of the transformer contains information related to its structure. It is easier to identify the resonance frequency in the vibration signal during the hammer test and power on than in the operation of the transformer, because the vibration caused by the load current does not ne...

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Autores principales: Ni, Ruizheng, Qiu, Ruichang, Jin, Zheming, Chen, Jie, Liu, Zhigang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584928/
https://www.ncbi.nlm.nih.gov/pubmed/36266473
http://dx.doi.org/10.1038/s41598-022-22519-z
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author Ni, Ruizheng
Qiu, Ruichang
Jin, Zheming
Chen, Jie
Liu, Zhigang
author_facet Ni, Ruizheng
Qiu, Ruichang
Jin, Zheming
Chen, Jie
Liu, Zhigang
author_sort Ni, Ruizheng
collection PubMed
description The resonant frequency of the transformer contains information related to its structure. It is easier to identify the resonance frequency in the vibration signal during the hammer test and power on than in the operation of the transformer, because the vibration caused by the load current does not need to be considered during the hammer test and power on. Therefore, an analysis method with simple calculation, fast calculation speed and easy real-time monitoring is needed to deal with these two non-stationary vibrations. Vibration monitoring can understand the health status of transformer in real time, improve the reliability of power supply and give early warning in the early stage of faults. A new frequency domain segmentation method is proposed in this paper. This method can effectively process the vibration signal of transformer and identify its resonant frequency. Eleven different load states are set on the transformer. The method proposed in this paper can extract the resonant frequency of the transformer from the hammering test signal. Compared with the original empirical wavelet transform method, this method can divide the frequency domain more effectively, has higher time–frequency resolution, and the running time of the modified method is shortened from 80 to 2 s. The universality of this method is proved by experiments on three different types of transformers.
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spelling pubmed-95849282022-10-22 Improved empirical wavelet transform (EWT) and its application in non-stationary vibration signal of transformer Ni, Ruizheng Qiu, Ruichang Jin, Zheming Chen, Jie Liu, Zhigang Sci Rep Article The resonant frequency of the transformer contains information related to its structure. It is easier to identify the resonance frequency in the vibration signal during the hammer test and power on than in the operation of the transformer, because the vibration caused by the load current does not need to be considered during the hammer test and power on. Therefore, an analysis method with simple calculation, fast calculation speed and easy real-time monitoring is needed to deal with these two non-stationary vibrations. Vibration monitoring can understand the health status of transformer in real time, improve the reliability of power supply and give early warning in the early stage of faults. A new frequency domain segmentation method is proposed in this paper. This method can effectively process the vibration signal of transformer and identify its resonant frequency. Eleven different load states are set on the transformer. The method proposed in this paper can extract the resonant frequency of the transformer from the hammering test signal. Compared with the original empirical wavelet transform method, this method can divide the frequency domain more effectively, has higher time–frequency resolution, and the running time of the modified method is shortened from 80 to 2 s. The universality of this method is proved by experiments on three different types of transformers. Nature Publishing Group UK 2022-10-20 /pmc/articles/PMC9584928/ /pubmed/36266473 http://dx.doi.org/10.1038/s41598-022-22519-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ni, Ruizheng
Qiu, Ruichang
Jin, Zheming
Chen, Jie
Liu, Zhigang
Improved empirical wavelet transform (EWT) and its application in non-stationary vibration signal of transformer
title Improved empirical wavelet transform (EWT) and its application in non-stationary vibration signal of transformer
title_full Improved empirical wavelet transform (EWT) and its application in non-stationary vibration signal of transformer
title_fullStr Improved empirical wavelet transform (EWT) and its application in non-stationary vibration signal of transformer
title_full_unstemmed Improved empirical wavelet transform (EWT) and its application in non-stationary vibration signal of transformer
title_short Improved empirical wavelet transform (EWT) and its application in non-stationary vibration signal of transformer
title_sort improved empirical wavelet transform (ewt) and its application in non-stationary vibration signal of transformer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584928/
https://www.ncbi.nlm.nih.gov/pubmed/36266473
http://dx.doi.org/10.1038/s41598-022-22519-z
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