Autors: Ivanov G., Mateev, V. M., Marinova, I. I.
Title: A Cascaded Neural Network Framework for Estimating Degree of Polymerization of Insulated Paper and DGA Analysis in Power Transformers
Keywords: 2-Furaldehyde, Degree of polymerization, DGA, fault diagnosis, insulation aging, neural networks, power transformers, predictive maintenance

Abstract: This paper presents a cascaded artificial neural network (ANN) framework designed to improve the estimation of insulation paper evaluation by estimation of degree of polymerization (DP) based on Furaldehyde (2-FAL) concentration data and diagnosing transformer faults through Dissolved Gas Analysis (DGA). The framework contains three specialized ANNs: a model for DP estimation from 2-FAL measurements data, a fault classifier based on DGA results, and a validator correlating predicted DP. A data set of 80 power transformers was used to train and test created ANNs. Integrating 2-FAL and DGA data within a cascaded ANNs architecture framework provides a foundation for developing predictive, data-driven maintenance strategies for improved transformer reliability.

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Issue

2025 19th International Conference on Electrical Machines, Drives and Power Systems, ELMA 2025 - Proceedings, pp. 1-5, 2025, Bulgaria, https://doi.org/10.1109/ELMA65795.2025.11083400

Copyright IEEE

Цитирания (Citation/s):
1. Hinov N., Solid-State Transformers in the Global Clean Energy Transition: Decarbonization Impact and Lifecycle Performance, 2026, Energies, issue 2, vol. 19, DOI 10.3390/en19020558, eissn 19961073 - 2026 - в издания, индексирани в Scopus

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus