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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">techtransp</journal-id><journal-title-group><journal-title xml:lang="ru">Техник транспорта: образование и практика</journal-title><trans-title-group xml:lang="en"><trans-title>Transport Technician: Education and Practice</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2687-1025</issn><issn pub-type="epub">2687-1033</issn><publisher><publisher-name>Federal state budget establishment additional professional education «Educational and instructional center for railway transportation»</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.46684/2687-1033.2022.3.176-182</article-id><article-id custom-type="elpub" pub-id-type="custom">techtransp-381</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ЦИФРОВИЗАЦИЯ, ИННОВАЦИИ И МОДЕЛИРОВАНИЕ РАЗВИТИЯ ТРАНСПОРТНЫХ СИСТЕМ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>DIGITALIZATION, INNOVATION AND MODELING OF THE DEVELOPMENT OF TRANSPORT SYSTEMS</subject></subj-group></article-categories><title-group><article-title>Реализация алгоритма модуляции пространственного вектора напряжения с помощью искусственной нейронной сети</article-title><trans-title-group xml:lang="en"><trans-title>Implementation of the spatial voltage vector modulation algorithm using an artificial neural network</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Чхор</surname><given-names>Ким Гым</given-names></name><name name-style="western" xml:lang="en"><surname>Chol</surname><given-names>Kim Gum</given-names></name></name-alternatives><bio xml:lang="ru"><p>Ким Гым Чхор — доктор технических наук, доцент, преподаватель.</p><p>Пхеньян, Хенчжесанский район, Хадан-1</p></bio><bio xml:lang="en"><p>Bionotes Kim Gum Chol — Dr. Sci. (Eng.), Associate Professor, lecturer.</p><p>North Korea, Pyongyang, Hengshan district, Hadan-1</p></bio><email xlink:type="simple">ttspo@umczdt.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Сен</surname><given-names>Ким Гым</given-names></name><name name-style="western" xml:lang="en"><surname>Song</surname><given-names>Kim Gum</given-names></name></name-alternatives><bio xml:lang="ru"><p>Ким Гым Сен — кандидат технических наук; преподаватель.</p><p>Пхеньян, Хенчжесанский район, Хадан-1</p></bio><bio xml:lang="en"><p>Kim Gum Song — Cand. Sci. (Econ.), lecturer.</p><p>North Korea, Pyongyang, Hengshan district, Hadan-1</p></bio><email xlink:type="simple">ttspo@umczdt.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Пхеньянский университет путей сообщения</institution><country>Северная Корея</country></aff><aff xml:lang="en"><institution>Pyongyang Transport University</institution><country>Korea, Democratic People's Republic of</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2022</year></pub-date><pub-date pub-type="epub"><day>01</day><month>07</month><year>2022</year></pub-date><volume>3</volume><issue>2</issue><fpage>176</fpage><lpage>182</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Чхор К., Сен К., 2022</copyright-statement><copyright-year>2022</copyright-year><copyright-holder xml:lang="ru">Чхор К., Сен К.</copyright-holder><copyright-holder xml:lang="en">Chol K., Song K.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.ttspo.ru/jour/article/view/381">https://www.ttspo.ru/jour/article/view/381</self-uri><abstract><p>Предложен алгоритм пространственно-векторной широтно-импульсной модуляции (ШИМ) (Space vector pulse width modulation — SVPWM) на основе искусственной нейронной сети.</p><p>В расчетах времени действия векторов напряжения по традиционному алгоритму SVPWM в качестве переменных используют значения относительного фазового угла на секторах, это вызывает сложности в их вычислении.</p><p>Для преодоления данных недостатков составлены математические модели, определяющие время открытия силового элемента по абсолютному фазовому углу на инверторном преобразователе. Поскольку математические модели имеют различные виды расчетных формул по секторам и невозможно выразить их в однозначной формуле, для устранения этих трудностей построена функция с помощью искусственной нейронной сети, с помощью которой можно обобщенно определять время открытия силового элемента по фазам A, B и C.</p><p>Независимо от секторов, алгоритм SVPWM устанавливает время открытия силового элемента по фазовому углу θ на инверторном преобразователе.</p><p>Проведена проверка обобщенной способности искусственной нейронной сети и ее точности, а также времени операции моделирования в среде MATLAB.</p><p>Предложенный способ позволяет достигнуть высокой точности регулирования скорости и значительно сократить время операции.</p><p>При управлении тяговым приводом электроподвижного состава с асинхронным тяговым двигателем можно уменьшить период регулирования, в итоге повышается тяговая способность электровозов и электропоездов метрополитена с векторным управлением или с прямым управлением моментом примерно в 1,1–1,2 раза.</p></abstract><trans-abstract xml:lang="en"><p>An algorithm for space vector pulse width modulation (SVPWM) based on an artiﬁcial neural network is proposed.</p><p>In calculating the action time of the voltage vectors according to the traditional SVPWM algorithm, the values of the relative phase angle on the sectors are used as variables, which causes diﬃculties in their calculation.</p><p>To overcome these shortcomings, mathematical models have been compiled that determine the opening time of the power element by the absolute phase angle on the inverter converter. Since mathematical models have different types of calculation formulas for sectors and it is impossible to express them in an unambiguous formula, to eliminate these diﬃculties, a function was built using an artiﬁcial neural network, with which you can generally determine the opening time of a power element in phases A, B and C.</p><p>Regardless of the sectors, the SVPWM algorithm sets the opening time of the power element according to the phase angle θ on the inverter converter.</p><p>The generalized ability of the artiﬁcial neural network and its accuracy, as well as the simulation operation time in the MATLAB environment, were tested.</p><p>The proposed method makes it possible to achieve high speed control accuracy and signiﬁcantly reduce the operation time.</p><p>When controlling the traction drive of an electric rolling stock with an asynchronous traction motor, it is possible to reduce the regulation period, as a result, the traction capacity of electric locomotives and metro electric trains with vector control or with direct torque control increases by about 1.1–1.2 times.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>SVPWM</kwd><kwd>искусственная нейронная сеть</kwd><kwd>асинхронный двигатель</kwd><kwd>инвертор</kwd><kwd>фазовый угол</kwd><kwd>время открытия и закрытия</kwd><kwd>вектор напряжения</kwd><kwd>контроль реального времени</kwd><kwd>алгоритм</kwd></kwd-group><kwd-group xml:lang="en"><kwd>SVPWM</kwd><kwd>artiﬁcial neural network</kwd><kwd>asynchronous motor</kwd><kwd>inverter</kwd><kwd>phase angle</kwd><kwd>opening and closing times</kwd><kwd>voltage vector</kwd><kwd>real time control</kwd><kwd>algorithm</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Kim J.S., Sul S.K. 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