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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| Дата: | 2016 |
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| Автори: | , , , |
| Формат: | Стаття |
| Мова: | English Russian |
| Опубліковано: |
National Technical University "Kharkiv Polytechnic Institute" and State Institution “Institute of Technical Problems of Magnetism of the National Academy of Sciences of Ukraine”
2016
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| Онлайн доступ: | http://eie.khpi.edu.ua/article/view/2074-272X.2016.1.12 |
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| Назва журналу: | Electrical Engineering & Electromechanics |
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Electrical Engineering & Electromechanics| id |
eiekhpieduua-article-62611 |
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eiekhpieduua-article-626112017-08-21T18:28:21Z NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS НЕЙРО-СЕТЕВОЕ МОДЕЛИРОВАНИЕ В ЗАДАЧАХ ПРОГНОЗИРОВАНИЯ РЕЖИМОВ РАБОТЫ ЭЛЕКТРИЧЕСКИХ СЕТЕЙ НЕЙРО-СЕТЕВОЕ МОДЕЛИРОВАНИЕ В ЗАДАЧАХ ПРОГНОЗИРОВАНИЯ РЕЖИМОВ РАБОТЫ ЭЛЕКТРИЧЕСКИХ СЕТЕЙ Moroz, A. N. Cheremisin, N. M. Cherkashina, V. V. Kholod, A. V. electric grid neural grid neuro-fuzzy grid temperature monitoring of air electric line prediction modes of electric grid 621.315 электрическая сеть нейросеть нейро-фаззи сеть температурный мониторинг воздушной линии прогнозирование режимов работы электрической сети 621.315 електрична мережа нейромережа нейро-фаззі мережа температурний моніторинг повітряної лінії прогнозування режимів роботи електричної мережі 621.315 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. В статье сформирована нейро-фаззи сеть с учетом температурного мониторинга воздушной линии. Отличительной особенностью, предложенной сети, являются возможность обработки информации, заданной в разных шкалах измерения, и высокое быстродействие для прогнозирования режимов работы электрической сети. У статті сформована нейро-фаззі мережа з урахуванням температурного моніторингу повітряної лінії. Відмінною особливістю, запропонованої мережі, є можливість обробки інформації, яку задано в різних шкалах вимірювання, і висока швидкодія для прогнозування режимів роботи електричної мережі. National Technical University "Kharkiv Polytechnic Institute" and State Institution “Institute of Technical Problems of Magnetism of the National Academy of Sciences of Ukraine” 2016-03-12 Article Article application/pdf application/pdf http://eie.khpi.edu.ua/article/view/2074-272X.2016.1.12 10.20998/2074-272X.2016.1.12 Electrical Engineering & Electromechanics; No. 1 (2016); 65-68 Электротехника и Электромеханика; № 1 (2016); 65-68 Електротехніка і Електромеханіка; № 1 (2016); 65-68 2309-3404 2074-272X en ru http://eie.khpi.edu.ua/article/view/2074-272X.2016.1.12/58141 http://eie.khpi.edu.ua/article/view/2074-272X.2016.1.12/58142 Copyright (c) 2016 A. N. Moroz, N. M. Cheremisin, V. V. Cherkashina, A. V. Kholod https://creativecommons.org/licenses/by-nc/4.0 |
| institution |
Electrical Engineering & Electromechanics |
| collection |
OJS |
| language |
English Russian |
| topic |
electric grid neural grid neuro-fuzzy grid temperature monitoring of air electric line prediction modes of electric grid 621.315 электрическая сеть нейросеть нейро-фаззи сеть температурный мониторинг воздушной линии прогнозирование режимов работы электрической сети 621.315 електрична мережа нейромережа нейро-фаззі мережа температурний моніторинг повітряної лінії прогнозування режимів роботи електричної мережі 621.315 |
| spellingShingle |
electric grid neural grid neuro-fuzzy grid temperature monitoring of air electric line prediction modes of electric grid 621.315 электрическая сеть нейросеть нейро-фаззи сеть температурный мониторинг воздушной линии прогнозирование режимов работы электрической сети 621.315 електрична мережа нейромережа нейро-фаззі мережа температурний моніторинг повітряної лінії прогнозування режимів роботи електричної мережі 621.315 Moroz, A. N. Cheremisin, N. M. Cherkashina, V. V. Kholod, A. V. NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS |
| topic_facet |
electric grid neural grid neuro-fuzzy grid temperature monitoring of air electric line prediction modes of electric grid 621.315 электрическая сеть нейросеть нейро-фаззи сеть температурный мониторинг воздушной линии прогнозирование режимов работы электрической сети 621.315 електрична мережа нейромережа нейро-фаззі мережа температурний моніторинг повітряної лінії прогнозування режимів роботи електричної мережі 621.315 |
| format |
Article |
| author |
Moroz, A. N. Cheremisin, N. M. Cherkashina, V. V. Kholod, A. V. |
| author_facet |
Moroz, A. N. Cheremisin, N. M. Cherkashina, V. V. Kholod, A. V. |
| author_sort |
Moroz, A. N. |
| 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 |
| title_alt |
НЕЙРО-СЕТЕВОЕ МОДЕЛИРОВАНИЕ В ЗАДАЧАХ ПРОГНОЗИРОВАНИЯ РЕЖИМОВ РАБОТЫ ЭЛЕКТРИЧЕСКИХ СЕТЕЙ НЕЙРО-СЕТЕВОЕ МОДЕЛИРОВАНИЕ В ЗАДАЧАХ ПРОГНОЗИРОВАНИЯ РЕЖИМОВ РАБОТЫ ЭЛЕКТРИЧЕСКИХ СЕТЕЙ |
| 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. |
| publisher |
National Technical University "Kharkiv Polytechnic Institute" and State Institution “Institute of Technical Problems of Magnetism of the National Academy of Sciences of Ukraine” |
| publishDate |
2016 |
| url |
http://eie.khpi.edu.ua/article/view/2074-272X.2016.1.12 |
| work_keys_str_mv |
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2024-06-01T14:38:16Z |
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2024-06-01T14:38:16Z |
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