Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory

The thyristor is the key device for the converter of the ultra-high-voltage DC (UHVDC) project to realize AC–DC conversion. The reliability of thyristors is directly related to the safe operation of the UHVDC transmission system. Due to the complex operating environment of the thyristor, there are m...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhaoyu Lei, Jianyi Guo, Yingfu Tian, Jiemin Yang, Yinwu Xiong, Jie Zhang, Ben Shang, Youping Fan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	UHVDC project thyristor aging state evaluation tensor domain theory data analysis Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/215317a1203a48a8af824298e33cf191
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:215317a1203a48a8af824298e33cf191
record_format	dspace
spelling	oai:doaj.org-article:215317a1203a48a8af824298e33cf1912021-11-11T15:41:37ZThyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory10.3390/electronics102127002079-9292https://doaj.org/article/215317a1203a48a8af824298e33cf1912021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2700https://doaj.org/toc/2079-9292The thyristor is the key device for the converter of the ultra-high-voltage DC (UHVDC) project to realize AC–DC conversion. The reliability of thyristors is directly related to the safe operation of the UHVDC transmission system. Due to the complex operating environment of the thyristor, there are many interrelated parameters that may affect the aging state of thyristors. To extract useful information from the massive high-dimensional data and further obtain the aging state of thyristors, a supervised tensor domain classification (STDC) method based on the adaptive syn-thetic sampling method, the gradient-boosting decision tree, and tensor domain theory is proposed in this paper. Firstly, the algorithm applies the continuous medium theory to analogize the aging state points of the thyristor to the mass points in the continuous medium. Then, the algorithm applies the concept of the tensor domain to identify the aging state of the thyristor and to transform the original state-identification problem into the state classification surface determination of the tensor domain. Secondly, a temporal fuzzy clustering algorithm is applied to realize automatic positioning of the classification surface of each tensor sub-domain. Furthermore, to solve the problem of unbalanced sample size between aging class data and normal class data in the state-identification domain, the improved adaptive synthetic sampling algorithm is applied to preprocess the data. The gradient-boosting decision tree algorithm is applied to solve the multi-classification problem of the thyristor. Finally, the comparison between the algorithm proposed and the conventional algorithm is performed through the field-test data provided by the CSG EHV Power Transmission Company of China’s Southern Power Grid. It is verified that the evaluation method proposed has higher recognition accuracy and can effectively classify the thyristor states.Zhaoyu LeiJianyi GuoYingfu TianJiemin YangYinwu XiongJie ZhangBen ShangYouping FanMDPI AGarticleUHVDC projectthyristoraging state evaluationtensor domain theorydata analysisElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2700, p 2700 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	UHVDC project thyristor aging state evaluation tensor domain theory data analysis Electronics TK7800-8360
spellingShingle	UHVDC project thyristor aging state evaluation tensor domain theory data analysis Electronics TK7800-8360 Zhaoyu Lei Jianyi Guo Yingfu Tian Jiemin Yang Yinwu Xiong Jie Zhang Ben Shang Youping Fan Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory
description	The thyristor is the key device for the converter of the ultra-high-voltage DC (UHVDC) project to realize AC–DC conversion. The reliability of thyristors is directly related to the safe operation of the UHVDC transmission system. Due to the complex operating environment of the thyristor, there are many interrelated parameters that may affect the aging state of thyristors. To extract useful information from the massive high-dimensional data and further obtain the aging state of thyristors, a supervised tensor domain classification (STDC) method based on the adaptive syn-thetic sampling method, the gradient-boosting decision tree, and tensor domain theory is proposed in this paper. Firstly, the algorithm applies the continuous medium theory to analogize the aging state points of the thyristor to the mass points in the continuous medium. Then, the algorithm applies the concept of the tensor domain to identify the aging state of the thyristor and to transform the original state-identification problem into the state classification surface determination of the tensor domain. Secondly, a temporal fuzzy clustering algorithm is applied to realize automatic positioning of the classification surface of each tensor sub-domain. Furthermore, to solve the problem of unbalanced sample size between aging class data and normal class data in the state-identification domain, the improved adaptive synthetic sampling algorithm is applied to preprocess the data. The gradient-boosting decision tree algorithm is applied to solve the multi-classification problem of the thyristor. Finally, the comparison between the algorithm proposed and the conventional algorithm is performed through the field-test data provided by the CSG EHV Power Transmission Company of China’s Southern Power Grid. It is verified that the evaluation method proposed has higher recognition accuracy and can effectively classify the thyristor states.
format	article
author	Zhaoyu Lei Jianyi Guo Yingfu Tian Jiemin Yang Yinwu Xiong Jie Zhang Ben Shang Youping Fan
author_facet	Zhaoyu Lei Jianyi Guo Yingfu Tian Jiemin Yang Yinwu Xiong Jie Zhang Ben Shang Youping Fan
author_sort	Zhaoyu Lei
title	Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory
title_short	Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory
title_full	Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory
title_fullStr	Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory
title_full_unstemmed	Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory
title_sort	thyristor aging-state-evaluation method based on state information and tensor domain theory
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/215317a1203a48a8af824298e33cf191
work_keys_str_mv	AT zhaoyulei thyristoragingstateevaluationmethodbasedonstateinformationandtensordomaintheory AT jianyiguo thyristoragingstateevaluationmethodbasedonstateinformationandtensordomaintheory AT yingfutian thyristoragingstateevaluationmethodbasedonstateinformationandtensordomaintheory AT jieminyang thyristoragingstateevaluationmethodbasedonstateinformationandtensordomaintheory AT yinwuxiong thyristoragingstateevaluationmethodbasedonstateinformationandtensordomaintheory AT jiezhang thyristoragingstateevaluationmethodbasedonstateinformationandtensordomaintheory AT benshang thyristoragingstateevaluationmethodbasedonstateinformationandtensordomaintheory AT youpingfan thyristoragingstateevaluationmethodbasedonstateinformationandtensordomaintheory
_version_	1718434202921205760

Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory

Ejemplares similares