Research on quick judgment of power system stability using grid hierarchy net

Although deep learning has been introduced in the stability simulation analysis of power system, the structure of model needs to be further studied. A good structure can reflect the essence and simplify the solving process, like convolutional neural network (CNN) for image recognition. In this paper...

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Autores principales:	Dongyu Shi, Lulu Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Power system Dynamic security assessment (DSA) Deep learning Model structure Grid hierarchy net (GHNet) Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f799fb04a371440c8982f7067082a738
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id	oai:doaj.org-article:f799fb04a371440c8982f7067082a738
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spelling	oai:doaj.org-article:f799fb04a371440c8982f7067082a7382021-11-26T04:34:20ZResearch on quick judgment of power system stability using grid hierarchy net2352-484710.1016/j.egyr.2021.08.174https://doaj.org/article/f799fb04a371440c8982f7067082a7382021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721007770https://doaj.org/toc/2352-4847Although deep learning has been introduced in the stability simulation analysis of power system, the structure of model needs to be further studied. A good structure can reflect the essence and simplify the solving process, like convolutional neural network (CNN) for image recognition. In this paper, a novel neural network model is proposed based on the power grid connection called grid hierarchy net (GHNet). The model can significantly reduce the number of trainable parameters while using more input variables to improve the accuracy of the model. Firstly, the construction method of GHNet is introduced based on electrical distance of stations. Then, some key issues are discussed including input and output selection. Finally, the actual data of the Northeast Power Grid of China was used to verify the feasibility and effectiveness of GHNet which meets the requirements of online security and stability analysis.Dongyu ShiLulu ZhangElsevierarticlePower systemDynamic security assessment (DSA)Deep learningModel structureGrid hierarchy net (GHNet)Electrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 25-32 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Power system Dynamic security assessment (DSA) Deep learning Model structure Grid hierarchy net (GHNet) Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Power system Dynamic security assessment (DSA) Deep learning Model structure Grid hierarchy net (GHNet) Electrical engineering. Electronics. Nuclear engineering TK1-9971 Dongyu Shi Lulu Zhang Research on quick judgment of power system stability using grid hierarchy net
description	Although deep learning has been introduced in the stability simulation analysis of power system, the structure of model needs to be further studied. A good structure can reflect the essence and simplify the solving process, like convolutional neural network (CNN) for image recognition. In this paper, a novel neural network model is proposed based on the power grid connection called grid hierarchy net (GHNet). The model can significantly reduce the number of trainable parameters while using more input variables to improve the accuracy of the model. Firstly, the construction method of GHNet is introduced based on electrical distance of stations. Then, some key issues are discussed including input and output selection. Finally, the actual data of the Northeast Power Grid of China was used to verify the feasibility and effectiveness of GHNet which meets the requirements of online security and stability analysis.
format	article
author	Dongyu Shi Lulu Zhang
author_facet	Dongyu Shi Lulu Zhang
author_sort	Dongyu Shi
title	Research on quick judgment of power system stability using grid hierarchy net
title_short	Research on quick judgment of power system stability using grid hierarchy net
title_full	Research on quick judgment of power system stability using grid hierarchy net
title_fullStr	Research on quick judgment of power system stability using grid hierarchy net
title_full_unstemmed	Research on quick judgment of power system stability using grid hierarchy net
title_sort	research on quick judgment of power system stability using grid hierarchy net
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/f799fb04a371440c8982f7067082a738
work_keys_str_mv	AT dongyushi researchonquickjudgmentofpowersystemstabilityusinggridhierarchynet AT luluzhang researchonquickjudgmentofpowersystemstabilityusinggridhierarchynet
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Research on quick judgment of power system stability using grid hierarchy net

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