Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging

Ahmed Sayadi, Djillali Mahi, Issouf Fofana (), Lakhdar Bessissa, Sid Ahmed Bessedik, Oscar Henry Arroyo-Fernandez and Jocelyn Jalbert
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Ahmed Sayadi: Laboratory of Studies and Development of Semiconductor and Dielectric Materials, LeDMaScD, University Amar Telidji of Laghouat, BP 37G Route of Ghardaïa, Laghouat 03000, Algeria
Djillali Mahi: Laboratory of Studies and Development of Semiconductor and Dielectric Materials, LeDMaScD, University Amar Telidji of Laghouat, BP 37G Route of Ghardaïa, Laghouat 03000, Algeria
Issouf Fofana: Research Chair on the Aging of Power Network Infrastructure (ViAHT), University of Québec, Saguenay, QC G7H 2B1, Canada
Lakhdar Bessissa: Materials Science and Informatics Laboratory (MSIL), University Ziane Achour of Djelfa, BP 3117 Route of Moudjbara, Djelfa 17000, Algeria
Sid Ahmed Bessedik: Laboratory for Analysis and Control of Energy Systems and Electrical Systems (LACOSERE), Laghouat University, Laghouat 03000, Algeria
Oscar Henry Arroyo-Fernandez: Research Institute d’Hydro-Québec, 1800 Boulevard Lionel-Boulet, Varennes, QC J3X 1S1, Canada
Jocelyn Jalbert: Research Institute d’Hydro-Québec, 1800 Boulevard Lionel-Boulet, Varennes, QC J3X 1S1, Canada

Energies, 2023, vol. 16, issue 18, 1-17

Abstract: The aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimization (MR-PSO) model. The aging of the paper insulation is monitored directly by the tensile strength and the degree of polymerization of the solid insulation and indirectly by chemical markers using 2-furfuraldehyde compound content in oil (2-FAL). A mathematical model is then developed to simulate the mechanical properties (degree of polymerization ( DP V ) and tensile index ( Tidx )) of the aged insulation paper. First, the datasets obtained from experimental results are used to create the MR model, and then the optimizer method PSO is used to optimize its coefficients in order to improve the MR model. Then, an ANN method is trained using the MR-PSO to create a nonlinear correlation between the DP V and the time, temperature, and 2-FAL values. The acquired results are assessed and compared with the experimental data. The model presents almost the same behavior. In particular, it has the capability to accurately simulate the nonlinear property behavior of insulation under thermal aging with an acceptable margin of error. Since the life expectancy of power transformers is directly related to that of the insulating paper, the proposed model can be useful to maintenance planners.

Keywords: modeling; prediction; mechanical behavior; particle swarm optimization; neural networks; power transformers (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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