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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">ipolytech</journal-id><journal-title-group><journal-title xml:lang="ru">iPolytech Journal</journal-title><trans-title-group xml:lang="en"><trans-title>iPolytech Journal</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2782-4004</issn><issn pub-type="epub">2782-6341</issn><publisher><publisher-name>Irkutsk National Research Technical University</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.21285/1814-3520-2021-1-57-65</article-id><article-id custom-type="elpub" pub-id-type="custom">ipolytech-465</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>ЭНЕРГЕТИКА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>POWER ENGINEERING</subject></subj-group></article-categories><title-group><article-title>Методы расчета надежности системы электроснабжения</article-title><trans-title-group xml:lang="en"><trans-title>Methods for calculating the reliability of power supply systems</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>Луковенко</surname><given-names>А. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Lukovenko</surname><given-names>A. S.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Луковенко Антон Сергеевич, кандидат технических наук, электромонтер по обслуживанию ПС 220 кВ  Тайга – Центральная ГПС</p><p>660111, г. Красноярск, ул. Пограничников,  105/5</p></bio><bio xml:lang="en"><p>Anton S. Lukovenko, Cand. Sci. (Eng.), Maintenance Electrician of 220 kV  Tayga Substation – Tsentralnaya Main Substation</p><p>105/5, Pogranichnikov St., Krasnoyarsk 660111</p></bio><email xlink:type="simple">anlukov2.0@mail.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>Зеньков</surname><given-names>И. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Zenkov</surname><given-names>I. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Зеньков Игорь Владимирович, доктор технических наук, профессор,  ведущий научный сотрудник</p><p>660049, г. Красноярск, пр. Мира, 53</p></bio><bio xml:lang="en"><p>Igor V. Zenkov, Dr. Sci. (Eng.), Professor, Leading Researcher</p><p>53 Mira pr., Krasnoyarsk 660049</p></bio><email xlink:type="simple">zenkoviv@mail.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Красноярское предприятие МЭС Сибири, филиал ПАО «ФСК ЕЭС»</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Krasnoyarsk enterprise MES Siberia Branch  of PJSC FGC UES</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Федеральный исследовательский центр информационных  и вычислительных технологий</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Federal Research Center for Information  and Computational Technologies</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2021</year></pub-date><pub-date pub-type="epub"><day>19</day><month>03</month><year>2021</year></pub-date><volume>25</volume><issue>1</issue><fpage>57</fpage><lpage>65</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Луковенко А.С., Зеньков И.В., 2021</copyright-statement><copyright-year>2021</copyright-year><copyright-holder xml:lang="ru">Луковенко А.С., Зеньков И.В.</copyright-holder><copyright-holder xml:lang="en">Lukovenko A.S., Zenkov I.V.</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://ipolytech.elpub.ru/jour/article/view/465">https://ipolytech.elpub.ru/jour/article/view/465</self-uri><abstract><p>Цель – определение показателей надежности системы электроснабжения с применением модели искусственной нейронной сети. Для разработки модели расчета технической надежности использованы алгоритм расчета показателей надежности систем электроснабжения, метод интенсивности отказов системы электроснабжения, модель прогнозирования с использованием искусственных нейронных сетей. Установлено, что систему электроснабжения образует разомкнутая радиальная схема электроснабжения. Определена интенсивность отказов подсистемы электроснабжения с помощью расчета интенсивности отказов i-го элемента подсистемы. В результате рассчитанной вероятности безотказной работы подсистемы для различных условий (5 временных интервалов) установлено, что при увеличении времени эксплуатации со 100 до 500 ч происходит линейное увеличение интенсивности отказов системы с 0,0051 до 0,0073 1/ч. Проведено сравнение полученных значений наработки до отказа основной и такой же резервной подсистемы, находящейся в ненагруженном режиме с абсолютно надежным переключателем (269,62 ч) с основной и такой же резервной подсистемы, находящейся в нагруженном режиме (202,21 ч), значения различны между собой на 67,41 ч, что говорит о более высокой степени надежности первого способа. Разработан программный комплекс «Прогноз_ИНС_2020». В результате сравнения результатов традиционного расчета интенсивности отказов системы электроснабжения и с помощью программного комплекса «Прогноз_ИНС_2020» получена допустимая точность не более 2,17%, что говорит о жизнеспособности данного программного комплекса при расчетах надежности на действующих энергетических предприятиях. Предлагаемые методы оценки технической надежности как с помощью традиционной модели, так и с применением модели на основе искусственной нейронной сети позволяют оценить состояние систем электроснабжения, что способствует предотвращению опасных аварийных ситуаций.  </p></abstract><trans-abstract xml:lang="en"><p>The aim was to determine the reliability indicators of a power supply system using an artificial neural network model. A model for calculating technical reliability was developed using the following methods: an algorithm for calculating reliability indicators of power supply systems, the method of failure rate of a power supply system and a forecasting model using artificial neural networks. It was established that a power supply system is formed by an open radial power supply circuit. The failure rate of the power supply subsystem was determined by calculating the failure rate of i-th element of the subsystem. As a result of calculating the probability of failure-free operation of the subsystem for various conditions (5 time intervals), it was found that with an increase in the operating time from 100 to 500 h, a linear increase in the rate of system failures occurs from 0.0051 to 0.0073 1/h. A comparison of the obtained mean-to-failure values of the main and the same backup subsystem in the unloaded mode with an absolutely reliable switch (269.62 h) with the main and the same backup subsystem in the loaded mode (202.21 h) was carried out. The results differ by 67.41 h, which indicates a higher degree of reliability of the first method. The software package Prognoz_INS_2020 was developed. An acceptable accuracy of no more than 2.17% was obtained by comparing the results of the conventional calculation of the failure rate of power supply systems and using the Prognoz_INS_2020 software package. This indicates the efficiency of the proposed software package in reliability calculations at operating energy enterprises. The proposed methods for assessing technical reliability both using the conventional model and a model based on an artificial neural network made it possible to assess the state of power supply systems, which helps to prevent dangerous emergencies. </p></trans-abstract><kwd-group xml:lang="ru"><kwd>система электроснабжения</kwd><kwd>наработка на отказ</kwd><kwd>интенсивность отказов</kwd><kwd>время восстановления</kwd><kwd>искусственная нейронная сеть</kwd></kwd-group><kwd-group xml:lang="en"><kwd>power supply system</kwd><kwd>mean time to failure (MTTF)</kwd><kwd>failure rate</kwd><kwd>recovery time</kwd><kwd>artificial neural network</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">Рыбаков В.В., Пешехонов Н.Е., Воронин А.Е. 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