Optimal Data Selection Rule Mining for Transformer Condition Assessment

The condition of the transformer can be accurately assessed based on dissolved gas online monitoring and analysis technology. With the widespread use of Dissolved Gas Analysis (DGA) technology, a large amount of dissolved gas time series data is obtained and forms DGA big data, which brings challeng...

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Autores principales:	Peng Zhang, Bo Qi, Mengyu Shao, Chengrong Li, Zhihai Rong, Jinxiang Chen, Hongbin Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Transformer optimal data selection rule condition assessment dissolved gas analysis data mining Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/16e032e3498142589af0e1bde762e5de
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id	oai:doaj.org-article:16e032e3498142589af0e1bde762e5de
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spelling	oai:doaj.org-article:16e032e3498142589af0e1bde762e5de2021-12-02T00:00:47ZOptimal Data Selection Rule Mining for Transformer Condition Assessment2169-353610.1109/ACCESS.2021.3126763https://doaj.org/article/16e032e3498142589af0e1bde762e5de2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9610028/https://doaj.org/toc/2169-3536The condition of the transformer can be accurately assessed based on dissolved gas online monitoring and analysis technology. With the widespread use of Dissolved Gas Analysis (DGA) technology, a large amount of dissolved gas time series data is obtained and forms DGA big data, which brings challenges to the data selection process of traditional condition assessment methods. Inaccurate results might be generated by selecting excessive or insufficient data from a long-span dissolved gas time series. The shorter dissolved gas time series fails to fully characterize the operation law of transformers, and the longer dissolved gas time series contains redundant information that results in inaccurate analysis models and excessive calculation consumption. This paper attempts to mine the optimal data selection rule of long-span dissolved gas time series for more accurate and efficient analysis. In this paper, C-C method was employed to calculate the phase space parameters of the time series. By analyzing the convergence of the correlation integral, the boundary conditions that maintain the time series phase space stability were revealed, and so was the relationship between the optimal length and the embedding dimension. The real cases show that the optimal data set provides the same accuracy and significantly improves the computation speed by 113.54 times. The optimal data selection rule eliminates redundant information in the long-span dissolved gas time series and retains the “optimal data”, which not only preserves the characteristics of the original data, but also improves the computation speed of transformer condition assessment.Peng ZhangBo QiMengyu ShaoChengrong LiZhihai RongJinxiang ChenHongbin WangIEEEarticleTransformeroptimal data selection rulecondition assessmentdissolved gas analysisdata miningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156962-156972 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Transformer optimal data selection rule condition assessment dissolved gas analysis data mining Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Transformer optimal data selection rule condition assessment dissolved gas analysis data mining Electrical engineering. Electronics. Nuclear engineering TK1-9971 Peng Zhang Bo Qi Mengyu Shao Chengrong Li Zhihai Rong Jinxiang Chen Hongbin Wang Optimal Data Selection Rule Mining for Transformer Condition Assessment
description	The condition of the transformer can be accurately assessed based on dissolved gas online monitoring and analysis technology. With the widespread use of Dissolved Gas Analysis (DGA) technology, a large amount of dissolved gas time series data is obtained and forms DGA big data, which brings challenges to the data selection process of traditional condition assessment methods. Inaccurate results might be generated by selecting excessive or insufficient data from a long-span dissolved gas time series. The shorter dissolved gas time series fails to fully characterize the operation law of transformers, and the longer dissolved gas time series contains redundant information that results in inaccurate analysis models and excessive calculation consumption. This paper attempts to mine the optimal data selection rule of long-span dissolved gas time series for more accurate and efficient analysis. In this paper, C-C method was employed to calculate the phase space parameters of the time series. By analyzing the convergence of the correlation integral, the boundary conditions that maintain the time series phase space stability were revealed, and so was the relationship between the optimal length and the embedding dimension. The real cases show that the optimal data set provides the same accuracy and significantly improves the computation speed by 113.54 times. The optimal data selection rule eliminates redundant information in the long-span dissolved gas time series and retains the “optimal data”, which not only preserves the characteristics of the original data, but also improves the computation speed of transformer condition assessment.
format	article
author	Peng Zhang Bo Qi Mengyu Shao Chengrong Li Zhihai Rong Jinxiang Chen Hongbin Wang
author_facet	Peng Zhang Bo Qi Mengyu Shao Chengrong Li Zhihai Rong Jinxiang Chen Hongbin Wang
author_sort	Peng Zhang
title	Optimal Data Selection Rule Mining for Transformer Condition Assessment
title_short	Optimal Data Selection Rule Mining for Transformer Condition Assessment
title_full	Optimal Data Selection Rule Mining for Transformer Condition Assessment
title_fullStr	Optimal Data Selection Rule Mining for Transformer Condition Assessment
title_full_unstemmed	Optimal Data Selection Rule Mining for Transformer Condition Assessment
title_sort	optimal data selection rule mining for transformer condition assessment
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/16e032e3498142589af0e1bde762e5de
work_keys_str_mv	AT pengzhang optimaldataselectionruleminingfortransformerconditionassessment AT boqi optimaldataselectionruleminingfortransformerconditionassessment AT mengyushao optimaldataselectionruleminingfortransformerconditionassessment AT chengrongli optimaldataselectionruleminingfortransformerconditionassessment AT zhihairong optimaldataselectionruleminingfortransformerconditionassessment AT jinxiangchen optimaldataselectionruleminingfortransformerconditionassessment AT hongbinwang optimaldataselectionruleminingfortransformerconditionassessment
_version_	1718403968589103104

Optimal Data Selection Rule Mining for Transformer Condition Assessment

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