State solutions for distribution systems and switching event using a neural network

Abstract Power flow calculations are an essential stage in many planning and control applications for distribution systems.To use these in control applications, however, the calculation time needs to be improved, and this can be done by the use of a trained ANN. This paper presents the consideration...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Arbel Yaniv, Avi Lin, David Raz, Yuval Beck
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2022
Materias:	artificial intelligence distribution networks smart power grids switching systems (control) Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
Acceso en línea:	https://doaj.org/article/7669a11dd82d4abba62b7dc13e33bd86
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7669a11dd82d4abba62b7dc13e33bd86
record_format	dspace
spelling	oai:doaj.org-article:7669a11dd82d4abba62b7dc13e33bd862021-12-02T14:01:23ZState solutions for distribution systems and switching event using a neural network1751-86951751-868710.1049/gtd2.12278https://doaj.org/article/7669a11dd82d4abba62b7dc13e33bd862022-01-01T00:00:00Zhttps://doi.org/10.1049/gtd2.12278https://doaj.org/toc/1751-8687https://doaj.org/toc/1751-8695Abstract Power flow calculations are an essential stage in many planning and control applications for distribution systems.To use these in control applications, however, the calculation time needs to be improved, and this can be done by the use of a trained ANN. This paper presents the considerations for constructing ANNs for DSs, and describes a method for training the system in order to support switching events representing a change in topology. The solutions for three DSs, balanced as well as unbalanced, are presented and the various considerations affecting the most appropriate ANN construction are discussed. The results are compared to the solution from the classical complex Newton‐Raphson and the fixed‐point iterative methods. The solutions have very high precision and good results are found for switched laterals. The computational performance is also compared and an improvement of two orders of magnitude is observed.Arbel YanivAvi LinDavid RazYuval BeckWileyarticleartificial intelligencedistribution networkssmart power gridsswitching systems (control)Distribution or transmission of electric powerTK3001-3521Production of electric energy or power. Powerplants. Central stationsTK1001-1841ENIET Generation, Transmission & Distribution, Vol 16, Iss 1, Pp 71-83 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence distribution networks smart power grids switching systems (control) Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
spellingShingle	artificial intelligence distribution networks smart power grids switching systems (control) Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841 Arbel Yaniv Avi Lin David Raz Yuval Beck State solutions for distribution systems and switching event using a neural network
description	Abstract Power flow calculations are an essential stage in many planning and control applications for distribution systems.To use these in control applications, however, the calculation time needs to be improved, and this can be done by the use of a trained ANN. This paper presents the considerations for constructing ANNs for DSs, and describes a method for training the system in order to support switching events representing a change in topology. The solutions for three DSs, balanced as well as unbalanced, are presented and the various considerations affecting the most appropriate ANN construction are discussed. The results are compared to the solution from the classical complex Newton‐Raphson and the fixed‐point iterative methods. The solutions have very high precision and good results are found for switched laterals. The computational performance is also compared and an improvement of two orders of magnitude is observed.
format	article
author	Arbel Yaniv Avi Lin David Raz Yuval Beck
author_facet	Arbel Yaniv Avi Lin David Raz Yuval Beck
author_sort	Arbel Yaniv
title	State solutions for distribution systems and switching event using a neural network
title_short	State solutions for distribution systems and switching event using a neural network
title_full	State solutions for distribution systems and switching event using a neural network
title_fullStr	State solutions for distribution systems and switching event using a neural network
title_full_unstemmed	State solutions for distribution systems and switching event using a neural network
title_sort	state solutions for distribution systems and switching event using a neural network
publisher	Wiley
publishDate	2022
url	https://doaj.org/article/7669a11dd82d4abba62b7dc13e33bd86
work_keys_str_mv	AT arbelyaniv statesolutionsfordistributionsystemsandswitchingeventusinganeuralnetwork AT avilin statesolutionsfordistributionsystemsandswitchingeventusinganeuralnetwork AT davidraz statesolutionsfordistributionsystemsandswitchingeventusinganeuralnetwork AT yuvalbeck statesolutionsfordistributionsystemsandswitchingeventusinganeuralnetwork
_version_	1718392148159627264

State solutions for distribution systems and switching event using a neural network

Ejemplares similares