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Diagnosing Disk-Space Variation in Distribution Power Transformer Windings Using Group Method of Data Handling Artificial Neural Networks

Author

Listed:
  • Omid Elahi

    (Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-39675, Iran)

  • Reza Behkam

    (Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-39675, Iran)

  • Gevork B. Gharehpetian

    (Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-39675, Iran)

  • Fazel Mohammadi

    (Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 1K3, Canada
    Electrical and Computer Engineering and Computer Science Department, University of New Haven, West Haven, CT 06516, USA)

Abstract

Monitoring centers in the smart grid exchange the collected data by sensors and smart meters to monitor the current conditions and performance of electric power components. Distribution Power Transformers (DPTs) have a key role in maintaining the integrity of power flow in the smart grid. Online monitoring of DPTs to detect possible faults can potentially increase the reliability of modern power systems. Mechanical defects of DPTs are the major issues in their proper operation that must be detected in their early stage of occurrence. One of the most effective solutions for diagnosing mechanical defects in DPTs is Frequency Response Analysis (FRA). In this study, an appropriate condition monitoring scheme for DPTs is developed to identify even minor winding defects. Disk-Space Variation (DSV), a common DPT windings fault, is applied to the 20 kV-winding of a 1.6 MVA DPT in various locations and with different severity. Their corresponding frequency responses are then computed, and all four components of the frequency responses, i.e., amplitude, argument, and real and imaginary parts, are evaluated. Different data-driven-based indices are implemented to extract appropriate feature vectors in the preprocessing stage. Group Method of Data Handling (GMDH) Artificial Neural Networks is proposed to assist monitoring centers in interpreting FRA signatures and identifying DPT defects at primary stages. GMDH has a data-dependent structure, which gives high flexibility to modeling nonlinear characteristics of FRA test results with different data sizes. It is demonstrated that the proposed approach is capable of accurately determining the fault location and fault severity. The proposed Artificial Intelligence (AI)-based approach is used to extract essential features from frequency response traces in order to detect the position and degree of Disk-Space Variation (DSV) in the DPT windings. The experimental results verify the effectiveness of the proposed methods in determining the severity and location of DSV defects.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8885-:d:982965
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    References listed on IDEAS

    as
    1. Yeunggurl Yoon & Yongju Son & Jintae Cho & SuHyeong Jang & Young-Geun Kim & Sungyun Choi, 2021. "High-Frequency Modeling of a Three-Winding Power Transformer Using Sweep Frequency Response Analysis," Energies, MDPI, vol. 14(13), pages 1-10, July.
    2. Satoru Miyazaki, 2021. "Detection of Winding Axial Displacement of a Real Transformer by Frequency Response Analysis without Fingerprint Data," Energies, MDPI, vol. 15(1), pages 1-14, December.
    3. Mehran Tahir & Stefan Tenbohlen, 2019. "A Comprehensive Analysis of Windings Electrical and Mechanical Faults Using a High-Frequency Model," Energies, MDPI, vol. 13(1), pages 1-25, December.
    4. Mehran Tahir & Stefan Tenbohlen, 2021. "Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements," Energies, MDPI, vol. 14(11), pages 1-25, May.
    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. ZhenHua Li & Yujie Zhang & Ahmed Abu-Siada & Xingxin Chen & Zhenxing Li & Yanchun Xu & Lei Zhang & Yue Tong, 2021. "Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network," Energies, MDPI, vol. 14(6), pages 1-14, March.
    7. Bonginkosi A. Thango & Agha F. Nnachi & Goodness A. Dlamini & Pitshou N. Bokoro, 2022. "A Novel Approach to Assess Power Transformer Winding Conditions Using Regression Analysis and Frequency Response Measurements," Energies, MDPI, vol. 15(7), pages 1-22, March.
    8. 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.
    9. Szymon Banaszak & Eugeniusz Kornatowski & Wojciech Szoka, 2021. "The Influence of the Window Width on FRA Assessment with Numerical Indices," Energies, MDPI, vol. 14(2), pages 1-18, January.
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