alternativeАльтернативная энергетика и экология (ISJAEE)Alternative Energy and Ecology (ISJAEE)1608-8298Международный издательский дом научной периодики "Спейс10.15518/isjaee.2026.02.200-210alternative-2786Research ArticleXI. ИННОВАЦИОННЫЕ РЕШЕНИЯ, ТЕХНОЛОГИИ, УСТРОЙСТВА И ИХ ВНЕДРЕНИЕ. 26. Инновационные решения в области энергетики и альтернативной энергетикиXI. INNOVATION SOLUTIONS, TECHNOLOGIES, FACILITIES AND THEIR INNOVATION. 26. Information solutions in the field of energy and alternative energyВероятная модель для оценки состояния электротрансформатора на основе интегрированных диагностических характеристикA probabilistic model for power transformer condition assessment based on integrated diagnostic featureshttps://orcid.org/0009-0003-8887-6831АбдуллабековаД. Р.AbdullabekovaD. R.

Абдуллабекова Дилафруз Рустамжоновна, кандидат технических наук, доцент кафедры «Систем энергообеспечения» 

100084, г. Ташкент, проспект Амира Темура, 108

Abdullabekova Dilafruz Rustamjonovna, Ph.D. candidate of technical sciences, associate professor of the department «Energy Supply Systems» 

100084, Tashkent, Amir Temur Avenue, 108

abdullabekova_94@mail.ruhttps://orcid.org/0000-0001-9290-5322КутбидиновО. М.KutbidinovO. M.

Кутбидинов Одилжон Мухаммаджонович, кандидат философии. Доцент кафедры электротехники

100174, г. Ташкент, Мирабадский район, ул. Чамбил, 1

Scopus Author ID: 57322406000

Kutbidinov Odiljon Muhammadjonovich, PhD. Associate Professor of the Department of Electrical Engineering 

100174, Tashkent, Mirabad District, Chambil Street, 1

НазербаеваМ. З.NazerbaevaM. Z.

Назербаева Мафтуна Зинаддиновна, ассистент кафедры «Систем энергообеспечения»

100084, г. Ташкент, проспект Амира Темура, 108

Nazerbaeva Maftuna Zinaddinovna, Assistant Professor at the Department of Power Systems 

100084, Tashkent, Amir Temur Avenue, 108

ШукуруллоевС. А.ShukurulloyevS. A.

Шукуруллоев Сарвар Анварович, ассистент кафедры «Систем энергообеспечения»

100084, г. Ташкент, проспект Амира Темура, 108

Shukurulloyev Sarvar Anvarovich, Assistant of the Department of «Energy Supply Systems»

100084, Tashkent, Amir Temur Avenue, 108

Ташкентский университет информационных технологий имени Мухаммада ал-ХоразмийУзбекистанTashkent University of Information Technologies named after Muhammad al-KhwarizmiUzbekistanТашкентский государственный университет транспортаУзбекистанTashkent State University of transportUzbekistan20261305202602200210Copyright © Международный издательский дом научной периодики "Спейс, 20262026Международный издательский дом научной периодики "СпейсМеждународный издательский дом научной периодики "Спейсhttps://www.isjaee.com/jour/about/submissions#copyrightNoticehttps://www.isjaee.com/jour/article/view/2786

Точная оценка технического состояния силовых трансформаторов является одной из фундаментальных проблем современных энергетических систем. Традиционные подходы к диагностике часто основаны на эвристических пороговых значениях или экспертной интерпретации отдельных показателей, что ограничивает их способность учитывать неопределенность и изменчивость измерений.

В данной статье предлагается вероятностная модель оценки состояния силовых трансформаторов, основанная на комплексном наборе диагностических характеристик. Состояние трансформатора моделируется как скрытая случайная переменная, выводимая из наблюдаемых диагностических данных в рамках байесовской модели. Предлагаемый подход обеспечивает математически строгое представление диагностической неопределенности и позволяет вероятностно интерпретировать состояния трансформатора. Исследованы аналитические свойства модели, а численные эксперименты с использованием синтетических данных демонстрируют ее устойчивость к шуму и корреляции характеристик. Представленная модель предназначена для теоретического анализа и электронного моделирования диагностики трансформаторов.

Accurate assessment of the technical condition of power transformers is a fundamental problem in modern power systems. Conventional diagnostic approaches are often based on heuristic thresholds or expert interpretation of individual indicators, which limits their ability to account for uncertainty and measurement variability.

This paper proposes a probabilistic model for power transformer condition assessment based on an integrated set of diagnostic features. The transformer condition is modeled as a hidden random variable inferred from observable diagnostic data within a Bayesian framework. The proposed approach provides a mathematically rigorous representation of diagnostic uncertainty and enables probabilistic interpretation of condition states. Analytical properties of the model are investigated, and numerical experiments using synthetic data demonstrate robustness with respect to noise and feature correlation. The presented framework is intended for theoretical analysis and electronic modeling of transformer diagnostics.

силовой трансформаторвероятностное моделированиеоценка состояниябайесовский выводдиагностические характеристикиpower transformerprobabilistic modelingcondition assessmentBayesian inferencediagnostic featuresReferencesBishop, C. M. Pattern Recognition and Machine Learning. Springer.. Bishop, C. M. Pattern Recognition and Machine Learning. Springer.. Murphy, K. P. Machine Learning: A Probabilistic Perspective. MIT Press.. Murphy, K. P. Machine Learning: A Probabilistic Perspective. MIT Press.. Kundur, P. Power Transformer Engineering. McGraw-Hill.. Kundur, P. Power Transformer Engineering. McGraw-Hill.. IEC 60599. Guide to the Interpretation of Dissolved Gas Analysis.. IEC 60599. Guide to the Interpretation of Dissolved Gas Analysis.. IEEE Std C57.104. Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers.. IEEE Std C57.104. Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers.. Zhou, H., Zhang, Y., & Li, P. 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The authors declare that there are no conflicts of interest present.