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An approach on lifetime estimation of distribution transformers based on degree of polymerization

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  • Ariannik, Mohamadreza
  • Razi-Kazemi, Ali A.
  • Lehtonen, Matti

Abstract

Lifetime of oil-immersed transformers is highly dependent on condition of paper insulation. This contribution is aimed to quantify the deterioration and ageing process of the paper insulation of distribution transformers based on degree of polymerization (DP). The proposed approach involves real operating conditions of a transformer such as variable ambient temperature, load factor, and moisture content of the paper insulation through calculation of hot-spot temperature to estimate remaining lifetime of the transformers. The results indicate that a DP profile obtained based on actual conditions is completely different to that usually discussed in other researches under completely constant conditions. Consequently, the proposed dynamic DP model could predict lifetime of the transformers more precisely based on real-time measurable quantities. In addition to the remnant lifetime estimation, the proposed dynamic DP profile is utilized to suggest the optimum time for implementing reductions in moisture content of the paper insulation through three scenarios regarding the practical limitations. Finally, reliability of the transformer is evaluated based on statistical data.

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  • Ariannik, Mohamadreza & Razi-Kazemi, Ali A. & Lehtonen, Matti, 2020. "An approach on lifetime estimation of distribution transformers based on degree of polymerization," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:reensy:v:198:y:2020:i:c:s0951832019308555
    DOI: 10.1016/j.ress.2020.106881
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    1. Nguyen, Khanh T. P. & Do, Phuc & Huynh, Khac Tuan & Bérenguer, Christophe & Grall, Antoine, 2019. "Joint optimization of monitoring quality and replacement decisions in condition-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 177-195.
    2. Youyuan Wang & Senlian Gong & Stanislaw Grzybowski, 2011. "Reliability Evaluation Method for Oil–Paper Insulation in Power Transformers," Energies, MDPI, vol. 4(9), pages 1-14, September.
    3. Å nipas, Mindaugas & Radziukynas, Virginijus & ValakeviÄ ius, Eimutis, 2017. "Modeling reliability of power systems substations by using stochastic automata networks," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 13-22.
    4. Liu, Bin & Liang, Zhenglin & Parlikad, Ajith Kumar & Xie, Min & Kuo, Way, 2017. "Condition-based maintenance for systems with aging and cumulative damage based on proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 200-209.
    5. Cheng, Jin & Wang, Jian & Wu, Xuezhou & Wang, Shuo, 2019. "An improved polynomial-based nonlinear variable importance measure and its application to degradation assessment for high-voltage transformer under imbalance data," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 175-191.
    6. Aizpurua, J.I. & Catterson, V.M. & Papadopoulos, Y. & Chiacchio, F. & D'Urso, D., 2017. "Supporting group maintenance through prognostics-enhanced dynamic dependability prediction," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 171-188.
    7. Briš, Radim & Byczanski, Petr & Goňo, Radomír & Rusek, Stanislav, 2017. "Discrete maintenance optimization of complex multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 80-89.
    8. Abdul Rahman, Fariz & Varuttamaseni, Athi & Kintner-Meyer, Michael & Lee, John C., 2013. "Application of fault tree analysis for customer reliability assessment of a distribution power system," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 76-85.
    9. Xing, Jinduo & Zeng, Zhiguo & Zio, Enrico, 2019. "A framework for dynamic risk assessment with condition monitoring data and inspection data," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    10. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
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    2. Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    3. Enze Zhang & Jiang Liu & Chaohai Zhang & Peijun Zheng & Yosuke Nakanishi & Thomas Wu, 2023. "State-of-Art Review on Chemical Indicators for Monitoring the Aging Status of Oil-Immersed Transformer Paper Insulation," Energies, MDPI, vol. 16(3), pages 1-31, January.
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    5. Zhengping Liang & Yan Fang & Hao Cheng & Yongbin Sun & Bo Li & Kai Li & Wenxuan Zhao & Zhongxu Sun & Yiyi Zhang, 2024. "Innovative Transformer Life Assessment Considering Moisture and Oil Circulation," Energies, MDPI, vol. 17(2), pages 1-21, January.

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