Assessing Polymerization-Based Diagnostics for Transformer Insulation via Comparative Statistics

Mohd Syukri Ali, Lilik Jamilatul Awalin (), Syahirah Abd Halim, Amirul Syafiq Abdul Jaafar, Ab Halim Abu Bakar, Issam A. Smadi and Saher Albatran
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Mohd Syukri Ali: UM Power Energy Dedicated Advanced Centre (UMPEDAC), Universiti Malaya, Kuala Lumpur 59990, Malaysia
Lilik Jamilatul Awalin: Faculty of Advanced Technology and Multidicipline, Universitas Airlangga, Surabaya 60155, Indonesia
Syahirah Abd Halim: Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Amirul Syafiq Abdul Jaafar: UM Power Energy Dedicated Advanced Centre (UMPEDAC), Universiti Malaya, Kuala Lumpur 59990, Malaysia
Ab Halim Abu Bakar: UM Power Energy Dedicated Advanced Centre (UMPEDAC), Universiti Malaya, Kuala Lumpur 59990, Malaysia
Issam A. Smadi: The Department of Electrical Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
Saher Albatran: The Department of Electrical Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan

Energies, 2025, vol. 18, issue 22, 1-17

Abstract: Power transformers are essential for grid stability and efficient energy transfer, but their reliability declines due to aging insulation systems made of paper and mineral oil. Monitoring techniques such as oil testing, dissolved gas analysis (DGA), and furan compound analysis help assess degradation, with the degree of polymerization (DP) serving as a key indicator of insulation health. This study evaluates five DP estimation methods, namely Chendong, Heisler & Banzer, Vaurchex, Pahlavanpour, and De Pablo, using six statistical metrics consisting of average, standard deviation, determination coefficient (DC), correlation coefficient (CC), t -test, and p -value. The Chendong method proved most robust, achieving DC = 0.677, CC = 0.878, and the lowest standard deviation (0.81), meeting all criteria. Heisler & Banzer followed with DC = 0.529 and CC = 0.878, though its higher deviation (1.04) affected consistency. Vaurchex and Pahlavanpour showed moderate performance (DC = 0.674 and 0.435) but failed to meet t -test and p -value thresholds. De Pablo ranked lowest (DC = 0.071), meeting only one criterion. By quantifying each method’s strengths and limitations, this paper offers a benchmarking framework to improve insulation diagnostics and guide maintenance decisions which ultimately enhance transformer reliability, asset management, and power system efficiency.

Keywords: power transformer; dissolved gas analysis; degree of polymerization; statistical analysis (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: 2025
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