energyЭнергетика. Известия высших учебных заведений и энергетических объединений СНГENERGETIKA. Proceedings of CIS higher education institutions and power engineering associations1029-74482414-0341BNTU10.21122/1029-7448-2023-66-3-233-245energy-2268Research ArticleЭЛЕКТРОЭНЕРГЕТИКАELECTRICAL POWER ENGINEERINGОпределение насыщения трансформатора тока на основе использования искусственной нейронной сетиCurrent Transformer Saturation Detection Method Based on Artificial Neural NetworkРумянцевЮ. B.RumiantsevYu. V.

Адрес для переписки: Румянцев Юрий Владимирович  –Белорусский национальный технический университет,пр-т Независимости, 65/2,220013, г. Минск, Республика Беларусь.Тел.: +375 17 326-89-51 y.rumiantsev@gmail.com

Address for correspondence:Rumiantsev Yury V. – Belаrusian National Technical University,65/2, Nezavisimosty Ave.,220013, Minsk, Republic of Belarus.Tel.: +375 17 326-89-51 y.rumiantsev@gmail.com

y.rumiantsev@gmail.comБелорусский национальный технический университет,БеларусьBelarusian National Technical UniversityBelarus202301062023663233245Copyright © Румянцев Ю.B., 20232023Румянцев Ю.B.Rumiantsev Y.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://energy.bntu.by/jour/article/view/2268

При насыщении трансформатора тока, преимущественно вследствие наличия экспоненциально затухающей апериодической составляющей в токе повреждения, его вторичный ток имеет характерную непериодическую искаженную форму, существенно отличающуюся от его первичной (истинной) формы, что ведет к занижению вычисляемого релейной защитой значения вторичного тока по сравнению с его истинным значением. Указанное приводит к затягиванию   времени   срабатывания или вовсе к отказу  функционирования  устройств 

релейной защиты, так как уставки и алгоритмы релейной защиты рассчитаны и построены соответственно из предположения о том, что форма сигнала вторичного тока является синусоидальной и пропорциональной первичному. А поскольку в общем случае при использовании классических электромагнитных трансформаторов тока исключить возможность их насыщения невозможно, то выявление указанного режима функционирования является актуальной технической задачей. В статье предлагается использовать искусственную нейронную сеть, которая совместно с традиционным способом определения насыщения на основе сравнения значений соседних выборок вторичного тока позволяет реализовать быстрый и надежный детектор насыщения трансформатора тока. Детально рассмотрены этапы практической реализации такой искусственной нейронной сети. В среде имитационного моделирования MATLAB-Simulink методом вычислительного эксперимента выполнена проверка функционирования предложенного детектора, которая подтвердила, что он позволяет быстро и безошибочно определять насыщение в широком диапазоне изменения параметров энергосистемы и самого трансформатора тока. 

When current transformer is saturated, mainly due to the presence of an exponentially decaying DC component in the fault current, its secondary current has a distinctive distorted waveform which significantly differs from its primary (true) waveform. It leads to an underestimation of the secondary current value calculated by the relay protection compared to its true value. Thus, in its turn, results in trip time delay or even in a relay protection devices operation failure, since its settings and algorithms are calculated and designed on the assumption that the secondary current waveform is sinusoidal and proportional to the primary. And since, when using classical electromagnetic current transformer, it is impossible to exclude the possibility of its saturation, the detection of such abnormal condition is an urgent technical problem. The article proposes to use an artificial neural network for this purpose, which, together with the traditional method of saturation detection based on adjacent secondary current samples comparison, allows implementing a fast and reliable current transformer saturation detector. The article details the stages of the practical implementation of such an artificial neural network. The MATLAB-Simulink environment was used for assess the proposed saturation detector operation. The experiments that had been performed confirmed that proposed method provides fast and accurate saturation detection within the wide range of the power system and current transformer parameters change.

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The authors declare that there are no conflicts of interest present.