Identification method of cascading failure in high-proportion renewable energy systems based on deep learning

A high proportion of new energy sources are connected to the grid, which not only alleviates the energy shortage, but also brings hidden dangers to the safe and stable operation of the grid. Aiming at the inadequate consideration of uncertainty caused by new energy sources such as wind power being c...

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Autores principales:	Yuhong Zhu, Xiaoming Liu, Bo Chen, Donglei Sun, Dong Liu, Yongzhi Zhou
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	High proportion of renewable energy Voltage-dominant Cascading failures High/low voltage ride through Neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e3278b04569b427998f3a16203ecda96
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spelling	oai:doaj.org-article:e3278b04569b427998f3a16203ecda962021-12-04T04:34:48ZIdentification method of cascading failure in high-proportion renewable energy systems based on deep learning2352-484710.1016/j.egyr.2021.11.022https://doaj.org/article/e3278b04569b427998f3a16203ecda962022-05-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721011677https://doaj.org/toc/2352-4847A high proportion of new energy sources are connected to the grid, which not only alleviates the energy shortage, but also brings hidden dangers to the safe and stable operation of the grid. Aiming at the inadequate consideration of uncertainty caused by new energy sources such as wind power being connected to the grid, a method for identifying voltage-dominant cascading fault types is proposed. A voltage-dominant fault analysis model in the sense of probability is established. Based on the fault data, the nonlinear mapping relationship between the initial fault and the fault propagation characteristics is established through the neural network. Finally, the effectiveness of the method proposed in this paper is verified in the IEEE39-bus system, and the simulation results show that the method proposed in this paper can effectively identify the fault types of high-proportion new energy systems.Yuhong ZhuXiaoming LiuBo ChenDonglei SunDong LiuYongzhi ZhouElsevierarticleHigh proportion of renewable energyVoltage-dominantCascading failuresHigh/low voltage ride throughNeural networkElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 8, Iss , Pp 117-122 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	High proportion of renewable energy Voltage-dominant Cascading failures High/low voltage ride through Neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	High proportion of renewable energy Voltage-dominant Cascading failures High/low voltage ride through Neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yuhong Zhu Xiaoming Liu Bo Chen Donglei Sun Dong Liu Yongzhi Zhou Identification method of cascading failure in high-proportion renewable energy systems based on deep learning
description	A high proportion of new energy sources are connected to the grid, which not only alleviates the energy shortage, but also brings hidden dangers to the safe and stable operation of the grid. Aiming at the inadequate consideration of uncertainty caused by new energy sources such as wind power being connected to the grid, a method for identifying voltage-dominant cascading fault types is proposed. A voltage-dominant fault analysis model in the sense of probability is established. Based on the fault data, the nonlinear mapping relationship between the initial fault and the fault propagation characteristics is established through the neural network. Finally, the effectiveness of the method proposed in this paper is verified in the IEEE39-bus system, and the simulation results show that the method proposed in this paper can effectively identify the fault types of high-proportion new energy systems.
format	article
author	Yuhong Zhu Xiaoming Liu Bo Chen Donglei Sun Dong Liu Yongzhi Zhou
author_facet	Yuhong Zhu Xiaoming Liu Bo Chen Donglei Sun Dong Liu Yongzhi Zhou
author_sort	Yuhong Zhu
title	Identification method of cascading failure in high-proportion renewable energy systems based on deep learning
title_short	Identification method of cascading failure in high-proportion renewable energy systems based on deep learning
title_full	Identification method of cascading failure in high-proportion renewable energy systems based on deep learning
title_fullStr	Identification method of cascading failure in high-proportion renewable energy systems based on deep learning
title_full_unstemmed	Identification method of cascading failure in high-proportion renewable energy systems based on deep learning
title_sort	identification method of cascading failure in high-proportion renewable energy systems based on deep learning
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/e3278b04569b427998f3a16203ecda96
work_keys_str_mv	AT yuhongzhu identificationmethodofcascadingfailureinhighproportionrenewableenergysystemsbasedondeeplearning AT xiaomingliu identificationmethodofcascadingfailureinhighproportionrenewableenergysystemsbasedondeeplearning AT bochen identificationmethodofcascadingfailureinhighproportionrenewableenergysystemsbasedondeeplearning AT dongleisun identificationmethodofcascadingfailureinhighproportionrenewableenergysystemsbasedondeeplearning AT dongliu identificationmethodofcascadingfailureinhighproportionrenewableenergysystemsbasedondeeplearning AT yongzhizhou identificationmethodofcascadingfailureinhighproportionrenewableenergysystemsbasedondeeplearning
_version_	1718373001014018048

Identification method of cascading failure in high-proportion renewable energy systems based on deep learning

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