Deep-Learning-Assisted Topology Identification and Sensor Placement for Active Distribution Network

In response to the demand for identification of distribution network topology with a high percentage of renewable energy penetration, a distribution network topology analysis method based on decision trees and deep learning methods is proposed. First, the decision tree model is constructed to analyz...

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Autores principales:	Juhua Hong, Linyao Zhang, Yufei Yan, Zeqi Wang, Pengzhe Ren
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/65642770684b4d58accd371e5770ae6b
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id	oai:doaj.org-article:65642770684b4d58accd371e5770ae6b
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spelling	oai:doaj.org-article:65642770684b4d58accd371e5770ae6b2021-11-29T00:56:27ZDeep-Learning-Assisted Topology Identification and Sensor Placement for Active Distribution Network1563-514710.1155/2021/8942733https://doaj.org/article/65642770684b4d58accd371e5770ae6b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8942733https://doaj.org/toc/1563-5147In response to the demand for identification of distribution network topology with a high percentage of renewable energy penetration, a distribution network topology analysis method based on decision trees and deep learning methods is proposed. First, the decision tree model is constructed to analyze the importance of each node’s characteristics to the observability of the distribution network topology. Next, we arrange the node feature importance from large to small and select the node measurement data with high importance as the training sample set. Then, the principal component analysis (PCA)-deep belief network (DBN) model is used to analyze the changes in the observability of the distribution network topology, and the nodes are selected as the optimal location for the measurement device when the distribution network is completely observable. Finally, the IEEE-33 bus system with a high proportion of renewable energy is used to verify that the method proposed has a good effect in the identification of the distribution network topology.Juhua HongLinyao ZhangYufei YanZeqi WangPengzhe RenHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Juhua Hong Linyao Zhang Yufei Yan Zeqi Wang Pengzhe Ren Deep-Learning-Assisted Topology Identification and Sensor Placement for Active Distribution Network
description	In response to the demand for identification of distribution network topology with a high percentage of renewable energy penetration, a distribution network topology analysis method based on decision trees and deep learning methods is proposed. First, the decision tree model is constructed to analyze the importance of each node’s characteristics to the observability of the distribution network topology. Next, we arrange the node feature importance from large to small and select the node measurement data with high importance as the training sample set. Then, the principal component analysis (PCA)-deep belief network (DBN) model is used to analyze the changes in the observability of the distribution network topology, and the nodes are selected as the optimal location for the measurement device when the distribution network is completely observable. Finally, the IEEE-33 bus system with a high proportion of renewable energy is used to verify that the method proposed has a good effect in the identification of the distribution network topology.
format	article
author	Juhua Hong Linyao Zhang Yufei Yan Zeqi Wang Pengzhe Ren
author_facet	Juhua Hong Linyao Zhang Yufei Yan Zeqi Wang Pengzhe Ren
author_sort	Juhua Hong
title	Deep-Learning-Assisted Topology Identification and Sensor Placement for Active Distribution Network
title_short	Deep-Learning-Assisted Topology Identification and Sensor Placement for Active Distribution Network
title_full	Deep-Learning-Assisted Topology Identification and Sensor Placement for Active Distribution Network
title_fullStr	Deep-Learning-Assisted Topology Identification and Sensor Placement for Active Distribution Network
title_full_unstemmed	Deep-Learning-Assisted Topology Identification and Sensor Placement for Active Distribution Network
title_sort	deep-learning-assisted topology identification and sensor placement for active distribution network
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/65642770684b4d58accd371e5770ae6b
work_keys_str_mv	AT juhuahong deeplearningassistedtopologyidentificationandsensorplacementforactivedistributionnetwork AT linyaozhang deeplearningassistedtopologyidentificationandsensorplacementforactivedistributionnetwork AT yufeiyan deeplearningassistedtopologyidentificationandsensorplacementforactivedistributionnetwork AT zeqiwang deeplearningassistedtopologyidentificationandsensorplacementforactivedistributionnetwork AT pengzheren deeplearningassistedtopologyidentificationandsensorplacementforactivedistributionnetwork
_version_	1718407711420317696

Deep-Learning-Assisted Topology Identification and Sensor Placement for Active Distribution Network

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