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Comparison of Impulse Wave and Sweep Frequency Response Analysis Methods for Diagnosis of Transformer Winding Faults

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  • Qing Yang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Peiyu Su

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Yong Chen

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

Abstract

Monitoring of winding faults is the most important item used to determine the maintenance status of a transformer. Commonly used methods for winding-fault diagnosis require the transformer to exit operation before testing and an external exciting signal, whether the transformer is malfunctioning or not. However, if an overvoltage signal can be regarded as a broadband excitation source for fault diagnosis, then the interference caused by signal injection can be eliminated without the need for additional pulse or impulse signals. In this paper, a tapped transformer is designed and test platforms are built to compare winding diagnoses using the impulse wave and sweep frequency response analysis methods by recording voltage responses on both the high- and low-voltage sides and calculating the respective transfer functions. Based on comparison of statistical indicators, it is found that the sensitivities of both methods are similar for detecting conditions of winding-ground and winding-interlayer short circuits. It is concluded that it is feasible to use a transient overvoltage monitoring system for winding-fault diagnosis.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:431-:d:94231
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    References listed on IDEAS

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    6. Saleh Alsuhaibani & Yasin Khan & Abderrahmane Beroual & Nazar Hussain Malik, 2016. "A Review of Frequency Response Analysis Methods for Power Transformer Diagnostics," Energies, MDPI, vol. 9(11), pages 1-17, October.
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    Cited by:

    1. Marek Florkowski & Jakub Furgał & Maciej Kuniewski, 2020. "Propagation of Overvoltages in the Form of Impulse, Chopped and Oscillating Waveforms in Transformer Windings—Time and Frequency Domain Approach," Energies, MDPI, vol. 13(2), pages 1-16, January.
    2. 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.
    3. 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.
    4. Omid Elahi & Reza Behkam & Gevork B. Gharehpetian & Fazel Mohammadi, 2022. "Diagnosing Disk-Space Variation in Distribution Power Transformer Windings Using Group Method of Data Handling Artificial Neural Networks," Energies, MDPI, vol. 15(23), pages 1-32, November.
    5. Song Wang & Shuang Wang & Ying Cui & Jie Long & Fuqiang Ren & Shengchang Ji & Shuhong Wang, 2020. "An Experimental Study of the Sweep Frequency Impedance Method on the Winding Deformation of an Onsite Power Transformer," Energies, MDPI, vol. 13(14), pages 1-13, July.
    6. Dante Ruiz-Robles & Vicente Venegas-Rebollar & Adolfo Anaya-Ruiz & Edgar L. Moreno-Goytia & Juan R. Rodríguez-Rodríguez, 2018. "Design and Prototyping Medium-Frequency Transformers Featuring a Nanocrystalline Core for DC–DC Converters," Energies, MDPI, vol. 11(8), pages 1-17, August.
    7. Jun Jiang & Mingxin Zhao & Chaohai Zhang & Min Chen & Haojun Liu & Ricardo Albarracín, 2018. "Partial Discharge Analysis in High-Frequency Transformer Based on High-Frequency Current Transducer," Energies, MDPI, vol. 11(8), pages 1-13, August.
    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.

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