Reliable Estimation for Health Index of Transformer Oil Based on Novel Combined Predictive Maintenance Techniques

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2022-03-02
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Mcode
Degree programme
Language
en
Pages
20
25954-25972
Series
IEEE Access, Volume 10
Abstract
Transformer oil insulation condition may be deteriorated due to electrical and thermal faults, which may lead to transformer failure and system outage. In this regard, the first part of this paper presents comprehensive maintenance for power transformers aiming to diagnose transformer faults more accurately. Specifically, it aims to identify incipient faults in power transformers using what is known as dissolved gas analysis (DGA) with a new proposed integrated method. This proposed method for DGA is implemented based on the integration among the results of five different DGA techniques; 1) conditional probability, 2) clustering, 3) Duval triangle, 4) Roger's four ratios refined, and 5) artificial neural network. Accordingly, this proposed integrated DGA method could improve the overall accuracy by 93.6% compared to the existing DGA techniques. In addition, the second part used for predictive maintenance is based on determining the health index for five new transformers and an aged power transformer (66/11 kV, 40 MVA) filled with NYTRO 10XN oil by evaluating the breakdown voltage, DGA, moisture content, and acidity for the oil. In the breakdown voltage test, two practical types of transformer oil; NYTRO 10XN and HyVolt III alongside their mixtures are estimated and compared. In addition, aged oil samples extracted from a real case study in-service transformer during operation with different aged durations; 9, 10, 11, 12, and 13 years, are tested for breakdown voltage, and then compared with fresh oil samples. For DGA, a temperature rise test is performed on the five new transformers with a comparison between dissolved gases before and after the temperature rise. In addition, winding resistance is measured after the temperature rise. Also, acidity and moisture are measured for oils extracted from the new five transformers and from the 13-year in-service transformer for studying their health index. The health index of the transformer insulation system is examined using only DGA and DGA plus breakdown voltage (BDV), moisture, and acidity. The results show that by using DGA plus BDV, moisture, and acidity, the health index provides reliable estimation results compared to using only DGA.
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Keywords
Dissolved gas analysis; Breakdown voltage; Temperature rise; Health index, Fault diagnosis, Predictive maintenance, Transformer oil
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Citation
Badawi, M, Ibrahim, S A, Mansour, D E A, EL-Faraskoury, A, Ward, S A, Mahmoud, K, Lehtonen, M & Darwish, M M F 2022, ' Reliable Estimation for Health Index of Transformer Oil Based on Novel Combined Predictive Maintenance Techniques ', IEEE Access, vol. 10, pp. 25954-25972 . https://doi.org/10.1109/ACCESS.2022.3156102