NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS

Purpose. Form a neuro-fuzzy network based on temperature monitoring of overhead transmission line for the prediction modes of the electrical network. Methodology. To predict the load capacity of the overhead line architecture provides the use of neuro-fuzzy network based on temperature monitoring of...

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Autores principales:	A.N. Moroz, N.M. Cheremisin, V.V. Cherkashina, A.V. Kholod
Formato:	article
Lenguaje:	EN RU UK
Publicado:	National Technical University "Kharkiv Polytechnic Institute" 2016
Materias:	electric grid neural grid neuro-fuzzy grid temperature monitoring of air electric line prediction modes of electric grid Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/787535615ab24ed98b1e1b3d383194b8
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spelling	oai:doaj.org-article:787535615ab24ed98b1e1b3d383194b82021-12-02T17:29:44ZNEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS10.20998/2074-272X.2016.1.122074-272X2309-3404https://doaj.org/article/787535615ab24ed98b1e1b3d383194b82016-03-01T00:00:00Zhttp://eie.khpi.edu.ua/article/view/2074-272X.2016.1.12/58141https://doaj.org/toc/2074-272Xhttps://doaj.org/toc/2309-3404Purpose. Form a neuro-fuzzy network based on temperature monitoring of overhead transmission line for the prediction modes of the electrical network. Methodology. To predict the load capacity of the overhead line architecture provides the use of neuro-fuzzy network based on temperature monitoring of overhead line. The proposed neuro-fuzzy network has a four-layer architecture with direct transmission of information. To create a full mesh network architecture based on hybrid neural elements with power estimation accuracy of the following two stages of the procedure: - in the first stage a core network (without power estimation accuracy) is generated; - in the second stage architecture and network parameters are fixed obtained during the first stage, and it is added to the block estimation accuracy, the input signals which are all input, internal and output signals of the core network, as well as additional input signals. Results. Formed neuro-fuzzy network based on temperature monitoring of overhead line. Originality. A distinctive feature of the proposed network is the ability to process information specified in the different scales of measurement, and high performance for prediction modes mains. Practical value. The monitoring system will become a tool parameter is measuring the temperature of the wire, which will, based on a retrospective analysis of the accumulated information on the parameters to predict the thermal resistance of the HV line and as a result carry out the calculation of load capacity in real time.A.N. MorozN.M. CheremisinV.V. CherkashinaA.V. KholodNational Technical University "Kharkiv Polytechnic Institute"articleelectric gridneural gridneuro-fuzzy gridtemperature monitoring of air electric lineprediction modes of electric gridElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENRUUKElectrical engineering & Electromechanics, Iss 1, Pp 65-68 (2016)
institution	DOAJ
collection	DOAJ
language	EN RU UK
topic	electric grid neural grid neuro-fuzzy grid temperature monitoring of air electric line prediction modes of electric grid Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	electric grid neural grid neuro-fuzzy grid temperature monitoring of air electric line prediction modes of electric grid Electrical engineering. Electronics. Nuclear engineering TK1-9971 A.N. Moroz N.M. Cheremisin V.V. Cherkashina A.V. Kholod NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS
description	Purpose. Form a neuro-fuzzy network based on temperature monitoring of overhead transmission line for the prediction modes of the electrical network. Methodology. To predict the load capacity of the overhead line architecture provides the use of neuro-fuzzy network based on temperature monitoring of overhead line. The proposed neuro-fuzzy network has a four-layer architecture with direct transmission of information. To create a full mesh network architecture based on hybrid neural elements with power estimation accuracy of the following two stages of the procedure: - in the first stage a core network (without power estimation accuracy) is generated; - in the second stage architecture and network parameters are fixed obtained during the first stage, and it is added to the block estimation accuracy, the input signals which are all input, internal and output signals of the core network, as well as additional input signals. Results. Formed neuro-fuzzy network based on temperature monitoring of overhead line. Originality. A distinctive feature of the proposed network is the ability to process information specified in the different scales of measurement, and high performance for prediction modes mains. Practical value. The monitoring system will become a tool parameter is measuring the temperature of the wire, which will, based on a retrospective analysis of the accumulated information on the parameters to predict the thermal resistance of the HV line and as a result carry out the calculation of load capacity in real time.
format	article
author	A.N. Moroz N.M. Cheremisin V.V. Cherkashina A.V. Kholod
author_facet	A.N. Moroz N.M. Cheremisin V.V. Cherkashina A.V. Kholod
author_sort	A.N. Moroz
title	NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS
title_short	NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS
title_full	NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS
title_fullStr	NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS
title_full_unstemmed	NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS
title_sort	neural network modeling in problems of prediction modes of electrical grids
publisher	National Technical University "Kharkiv Polytechnic Institute"
publishDate	2016
url	https://doaj.org/article/787535615ab24ed98b1e1b3d383194b8
work_keys_str_mv	AT anmoroz neuralnetworkmodelinginproblemsofpredictionmodesofelectricalgrids AT nmcheremisin neuralnetworkmodelinginproblemsofpredictionmodesofelectricalgrids AT vvcherkashina neuralnetworkmodelinginproblemsofpredictionmodesofelectricalgrids AT avkholod neuralnetworkmodelinginproblemsofpredictionmodesofelectricalgrids
_version_	1718380735854804992

NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS

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