IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION

IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION

Purpose. To consider problems of electric machines optimization within a wide range of many variables variation as well as the presence of many calculation constraints in a single-criteria optimization search tasks. Results. A structural model for optimizing electric machines of arbitrary type using...

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Date:2019
Main Authors: Pliugin, V. E., Sukhonos, M., Pan, M., Petrenko, A. N., Petrenko, N. Ya.
Format: Article
Language:English
Published: National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2019
Subjects:
electrical machines
optimization
algorithm
data set
machine learning
Microsoft Azure
cloud computing
629.429.3
621.313
Online Access:http://eie.khpi.edu.ua/article/view/2074-272X.2019.1.04
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Journal Title:Electrical Engineering & Electromechanics

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Electrical Engineering & Electromechanics
id eiekhpieduua-article-156816
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spelling eiekhpieduua-article-1568162019-02-17T17:29:46Z IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION Pliugin, V. E. Sukhonos, M. Pan, M. Petrenko, A. N. Petrenko, N. Ya. electrical machines optimization algorithm data set machine learning Microsoft Azure cloud computing 629.429.3 621.313 электрические машины оптимизация алгоритм набор данных машинное обучение Microsoft Azure облачные расчеты 629.429.3 621.313 Purpose. To consider problems of electric machines optimization within a wide range of many variables variation as well as the presence of many calculation constraints in a single-criteria optimization search tasks. Results. A structural model for optimizing electric machines of arbitrary type using Microsoft Azure machine learning technology has been developed. The obtained results, using several optimization methods from the Microsoft Azure database are demonstrated. The advantages of cloud computing and optimization based on remote servers are shown. The results of statistical analysis of the results are given. Originality. Microsoft Azure machine learning technology was used for electrical machines optimization for the first time. Recommendations for modifying standard algorithms, offered by Microsoft Azure are given. Practical value. Significant time reduction and resources spent on the optimization of electrical machines in a wide range of variable variables. Reducing the time to develop optimization algorithms. The possibility of automatic statistical analysis of the results after performing optimization calculations.  Рассмотрены проблемы оптимизации электрических машин при широком диапазоне варьирования многих переменных, наличии большого числа вычисляемых ограничений, в однокритериальных задачах оптимизационного поиска. Разработана структурная модель оптимизации электрических машин произвольного типа с применением технологии машинного обучения Microsoft Azure. Продемонстрированы результаты, полученные с использованием нескольких методов оптимизации из базы Microsoft Azure. Показаны преимущества облачных расчетов и оптимизации на базе удаленных серверов. Приведенные результаты касаются решения однокритериальной задачи оптимизации с двумя переменными. Даны результаты статистического анализа полученных результатов. Даны рекомендации по применению машинного обучения Microsoft Azure в проектировании и оптимизации электрических машин.  National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2019-02-17 Article Article application/pdf http://eie.khpi.edu.ua/article/view/2074-272X.2019.1.04 10.20998/2074-272X.2019.1.04 Electrical Engineering & Electromechanics; No. 1 (2019); 23-28 Электротехника и Электромеханика; № 1 (2019); 23-28 Електротехніка і Електромеханіка; № 1 (2019); 23-28 2309-3404 2074-272X en http://eie.khpi.edu.ua/article/view/2074-272X.2019.1.04/156168 Copyright (c) 2019 V. E. Pliugin, M. Sukhonos, M. Pan, A. N. Petrenko, N. Ya. Petrenko https://creativecommons.org/licenses/by-nc/4.0
institution Electrical Engineering & Electromechanics
baseUrl_str
datestamp_date 2019-02-17T17:29:46Z
collection OJS
language English
topic electrical machines
optimization
algorithm
data set
machine learning
Microsoft Azure
cloud computing
629.429.3
621.313
spellingShingle electrical machines
optimization
algorithm
data set
machine learning
Microsoft Azure
cloud computing
629.429.3
621.313
Pliugin, V. E.
Sukhonos, M.
Pan, M.
Petrenko, A. N.
Petrenko, N. Ya.
IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
topic_facet electrical machines
optimization
algorithm
data set
machine learning
Microsoft Azure
cloud computing
629.429.3
621.313
электрические машины
оптимизация
алгоритм
набор данных
машинное обучение
Microsoft Azure
облачные расчеты
629.429.3
621.313
format Article
author Pliugin, V. E.
Sukhonos, M.
Pan, M.
Petrenko, A. N.
Petrenko, N. Ya.
author_facet Pliugin, V. E.
Sukhonos, M.
Pan, M.
Petrenko, A. N.
Petrenko, N. Ya.
author_sort Pliugin, V. E.
title IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
title_short IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
title_full IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
title_fullStr IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
title_full_unstemmed IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
title_sort implementing of microsoft azure machine learning technology for electric machines optimization
title_alt IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION
description Purpose. To consider problems of electric machines optimization within a wide range of many variables variation as well as the presence of many calculation constraints in a single-criteria optimization search tasks. Results. A structural model for optimizing electric machines of arbitrary type using Microsoft Azure machine learning technology has been developed. The obtained results, using several optimization methods from the Microsoft Azure database are demonstrated. The advantages of cloud computing and optimization based on remote servers are shown. The results of statistical analysis of the results are given. Originality. Microsoft Azure machine learning technology was used for electrical machines optimization for the first time. Recommendations for modifying standard algorithms, offered by Microsoft Azure are given. Practical value. Significant time reduction and resources spent on the optimization of electrical machines in a wide range of variable variables. Reducing the time to develop optimization algorithms. The possibility of automatic statistical analysis of the results after performing optimization calculations. 
publisher National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine
publishDate 2019
url http://eie.khpi.edu.ua/article/view/2074-272X.2019.1.04
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AT sukhonosm implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization
AT panm implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization
AT petrenkoan implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization
AT petrenkonya implementingofmicrosoftazuremachinelearningtechnologyforelectricmachinesoptimization
first_indexed 2025-07-17T11:47:28Z
last_indexed 2025-07-17T11:47:28Z
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