IMPLEMENTING OF MICROSOFT AZURE MACHINE LEARNING TECHNOLOGY FOR ELECTRIC MACHINES OPTIMIZATION

• Дата: 2019
• Автори: Pliugin, V. E., Sukhonos, M., Pan, M., Petrenko, A. N., Petrenko, N. Ya.
• Формат: Стаття
• Мова: English
• Опубліковано: National Technical University "Kharkiv Polytechnic Institute" and State Institution “Institute of Technical Problems of Magnetism of the National Academy of Sciences of Ukraine” 2019
• Теми: 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; облачные расчеты
• Онлайн доступ: http://eie.khpi.edu.ua/article/view/2074-272X.2019.1.04
• Назва журналу: Electrical Engineering & Electromechanics

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.