Implementing of Microsoft Azure machine learning technology for electric machines optimization

• Published in: Електротехніка і електромеханіка
• Date: 2019
• Main Authors: Pliuhin, V., Sukhonos, M., Pan, M., Petrenko, O., Petrenko, M.
• Format: Article
• Language: English
• Published: Інститут технічних проблем магнетизму НАН України 2019
• Subjects: Електричні машини та апарати
• Online Access: https://nasplib.isofts.kiev.ua/handle/123456789/159032
• Journal Title: Digital Library of Periodicals of National Academy of Sciences of Ukraine
• Cite this: Implementing of Microsoft Azure machine learning technology for electric machines optimization / V. Pliuhin, M. Sukhonos, M. Pan, O. Petrenko, M. Petrenko // Електротехніка і електромеханіка. — 2019. — № 1. — С. 23-28. — Бібліогр.: 20 назв. — англ.

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.