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Induced Voltages Ratio-Based Algorithm for Fault Detection, and Faulted Phase and Winding Identification of a Three-Winding Power Transformer

Author

Listed:
  • Byung Eun Lee

    (Wind Energy Grid-Adaptive Technology Research Center, Chonbuk National University, Chonju 561-756, Korea)

  • Jung-Wook Park

    (The School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea)

  • Peter A. Crossley

    (The School of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK)

  • Yong Cheol Kang

    (Department of Electrical Engineering, Wind Energy Grid-Adaptive Technology Research Center, and Smart Grid Research Center, Chonbuk National University, Chonju 561-756, Korea)

Abstract

This paper proposes an algorithm for fault detection, faulted phase and winding identification of a three-winding power transformer based on the induced voltages in the electrical power system. The ratio of the induced voltages of the primary-secondary, primary-tertiary and secondary-tertiary windings is the same as the corresponding turns ratio during normal operating conditions, magnetic inrush, and over-excitation. It differs from the turns ratio during an internal fault. For a single phase and a three-phase power transformer with wye-connected windings, the induced voltages of each pair of windings are estimated. For a three-phase power transformer with delta-connected windings, the induced voltage differences are estimated to use the line currents, because the delta winding currents are practically unavailable. Six detectors are suggested for fault detection. An additional three detectors and a rule for faulted phase and winding identification are presented as well. The proposed algorithm can not only detect an internal fault, but also identify the faulted phase and winding of a three-winding power transformer. The various test results with Electromagnetic Transients Program (EMTP)-generated data show that the proposed algorithm successfully discriminates internal faults from normal operating conditions including magnetic inrush and over-excitation. This paper concludes by implementing the algorithm into a prototype relay based on a digital signal processor.

Suggested Citation

  • Byung Eun Lee & Jung-Wook Park & Peter A. Crossley & Yong Cheol Kang, 2014. "Induced Voltages Ratio-Based Algorithm for Fault Detection, and Faulted Phase and Winding Identification of a Three-Winding Power Transformer," Energies, MDPI, vol. 7(9), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:9:p:6031-6049:d:40221
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    Citations

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    Cited by:

    1. Radu Godina & Eduardo M. G. Rodrigues & João C. O. Matias & João P. S. Catalão, 2015. "Effect of Loads and Other Key Factors on Oil-Transformer Ageing: Sustainability Benefits and Challenges," Energies, MDPI, vol. 8(10), pages 1-40, October.
    2. 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.
    3. 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.
    4. 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.
    5. Zhongyong Zhao & Chao Tang & Qu Zhou & Lingna Xu & Yingang Gui & Chenguo Yao, 2017. "Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine," Energies, MDPI, vol. 10(12), pages 1-16, December.
    6. 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.
    7. 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.
    8. Jesus Serrano & Carlos A. Platero & Maximo López-Toledo & Ricardo Granizo, 2015. "A Novel Ground Fault Identification Method for 2 × 5 kV Railway Power Supply Systems," Energies, MDPI, vol. 8(7), pages 1-20, July.
    9. Qing Yang & Peiyu Su & Yong Chen, 2017. "Comparison of Impulse Wave and Sweep Frequency Response Analysis Methods for Diagnosis of Transformer Winding Faults," Energies, MDPI, vol. 10(4), pages 1-16, March.
    10. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.
    11. 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.
    12. 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.

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