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Reliability Analysis and Overload Capability Assessment of Oil-Immersed Power Transformers

Author

Listed:
  • Chen Wang

    (School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China)

  • Jie Wu

    (School of Mathematics and Computer Science, Northwest University for Nationalities, Lanzhou 730030, China)

  • Jianzhou Wang

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China)

  • Weigang Zhao

    (Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China
    School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Smart grids have been constructed so as to guarantee the security and stability of the power grid in recent years. Power transformers are a most vital component in the complicated smart grid network. Any transformer failure can cause damage of the whole power system, within which the failures caused by overloading cannot be ignored. This research gives a new insight into overload capability assessment of transformers. The hot-spot temperature of the winding is the most critical factor in measuring the overload capacity of power transformers. Thus, the hot-spot temperature is calculated to obtain the duration running time of the power transformers under overloading conditions. Then the overloading probability is fitted with the mature and widely accepted Weibull probability density function. To guarantee the accuracy of this fitting, a new objective function is proposed to obtain the desired parameters in the Weibull distributions. In addition, ten different mutation scenarios are adopted in the differential evolutionary algorithm to optimize the parameter in the Weibull distribution. The final comprehensive overload capability of the power transformer is assessed by the duration running time as well as the overloading probability. Compared with the previous studies that take no account of the overloading probability, the assessment results obtained in this research are much more reliable.

Suggested Citation

  • Chen Wang & Jie Wu & Jianzhou Wang & Weigang Zhao, 2016. "Reliability Analysis and Overload Capability Assessment of Oil-Immersed Power Transformers," Energies, MDPI, vol. 9(1), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:1:p:43-:d:62182
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    References listed on IDEAS

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    1. Fyrippis, Ioannis & Axaopoulos, Petros J. & Panayiotou, Gregoris, 2010. "Wind energy potential assessment in Naxos Island, Greece," Applied Energy, Elsevier, vol. 87(2), pages 577-586, February.
    2. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
    3. Wu, Jie & Wang, Jianzhou & Chi, Dezhong, 2013. "Wind energy potential assessment for the site of Inner Mongolia in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 215-228.
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    Cited by:

    1. Yiyi Zhang & Jiefeng Liu & Hanbo Zheng & Hua Wei & Ruijin Liao, 2017. "Study on Quantitative Correlations between the Ageing Condition of Transformer Cellulose Insulation and the Large Time Constant Obtained from the Extended Debye Model," Energies, MDPI, vol. 10(11), pages 1-17, November.
    2. Ruohan Gong & Jiangjun Ruan & Jingzhou Chen & Yu Quan & Jian Wang & Cihan Duan, 2017. "Analysis and Experiment of Hot-Spot Temperature Rise of 110 kV Three-Phase Three-Limb Transformer," Energies, MDPI, vol. 10(8), pages 1-12, July.
    3. Lefeng Cheng & Tao Yu & Guoping Wang & Bo Yang & Lv Zhou, 2018. "Hot Spot Temperature and Grey Target Theory-Based Dynamic Modelling for Reliability Assessment of Transformer Oil-Paper Insulation Systems: A Practical Case Study," Energies, MDPI, vol. 11(1), pages 1-26, January.
    4. Feng Yang & Lin Du & Lijun Yang & Chao Wei & Youyuan Wang & Liman Ran & Peng He, 2018. "A Parameterization Approach for the Dielectric Response Model of Oil Paper Insulation Using FDS Measurements," Energies, MDPI, vol. 11(3), pages 1-17, March.
    5. Jiefeng Liu & Hanbo Zheng & Yiyi Zhang & Hua Wei & Ruijin Liao, 2017. "Grey Relational Analysis for Insulation Condition Assessment of Power Transformers Based Upon Conventional Dielectric Response Measurement," Energies, MDPI, vol. 10(10), pages 1-16, October.
    6. Grzegorz Dombek & Zbigniew Nadolny & Piotr Przybylek & Radoslaw Lopatkiewicz & Agnieszka Marcinkowska & Lukasz Druzynski & Tomasz Boczar & Andrzej Tomczewski, 2020. "Effect of Moisture on the Thermal Conductivity of Cellulose and Aramid Paper Impregnated with Various Dielectric Liquids," Energies, MDPI, vol. 13(17), pages 1-17, August.
    7. Álvaro Jaramillo-Duque & Nicolás Muñoz-Galeano & José R. Ortiz-Castrillón & Jesús M. López-Lezama & Ricardo Albarracín-Sánchez, 2018. "Power Loss Minimization for Transformers Connected in Parallel with Taps Based on Power Chargeability Balance," Energies, MDPI, vol. 11(2), pages 1-12, February.
    8. Liang Zou & Yongkang Guo & Han Liu & Li Zhang & Tong Zhao, 2017. "A Method of Abnormal States Detection Based on Adaptive Extraction of Transformer Vibro-Acoustic Signals," Energies, MDPI, vol. 10(12), pages 1-18, December.
    9. Lingjie Sun & Yingyi Liu & Boyang Zhang & Yuwei Shang & Haiwen Yuan & Zhao Ma, 2016. "An Integrated Decision-Making Model for Transformer Condition Assessment Using Game Theory and Modified Evidence Combination Extended by D Numbers," Energies, MDPI, vol. 9(9), pages 1-22, August.
    10. Issouf Fofana & Yazid Hadjadj, 2018. "Power Transformer Diagnostics, Monitoring and Design Features," Energies, MDPI, vol. 11(12), pages 1-5, November.

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