IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i23p8885-d982965.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/23/8885/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/23/8885/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaoxia Liang & Ming Zhang & Guojin Feng & Duo Wang & Yuchun Xu & Fengshou Gu, 2023. "Few-Shot Learning Approaches for Fault Diagnosis Using Vibration Data: A Comprehensive Review," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
    2. Yunhe Luo & Xiaosong Zou & Wei Xiong & Xufeng Yuan & Kui Xu & Yu Xin & Ruoyu Zhang, 2023. "Dynamic State Evaluation Method of Power Transformer Based on Mahalanobis–Taguchi System and Health Index," Energies, MDPI, vol. 16(6), pages 1-16, March.
    3. 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.
    4. Zhannan Guo & Yinlin Hao & Hanwen Shi & Zhenyu Wu & Yuhu Wu & Ximing Sun, 2023. "A Fault Diagnosis Algorithm for the Dedicated Equipment Based on the CNN-LSTM Mechanism," Energies, MDPI, vol. 16(13), pages 1-16, July.
    5. 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.
    6. 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.
    7. Wenqi Ge & Chenchen Zhang & Yi Xie & Ming Yu & Youhua Wang, 2021. "Analysis of the Electromechanical Characteristics of Power Transformer under Different Residual Fluxes," Energies, MDPI, vol. 14(24), pages 1-22, December.
    8. 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.
    9. Lefa Zhao & Yafei Zhu & Tianyu Zhao, 2022. "Deep Learning-Based Remaining Useful Life Prediction Method with Transformer Module and Random Forest," Mathematics, MDPI, vol. 10(16), pages 1-15, August.
    10. 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.
    11. Sara Mantach & Abdulla Lutfi & Hamed Moradi Tavasani & Ahmed Ashraf & Ayman El-Hag & Behzad Kordi, 2022. "Deep Learning in High Voltage Engineering: A Literature Review," Energies, MDPI, vol. 15(14), pages 1-32, July.
    12. Mehran Tahir & Stefan Tenbohlen, 2023. "Transformer Winding Fault Classification and Condition Assessment Based on Random Forest Using FRA," Energies, MDPI, vol. 16(9), pages 1-16, April.
    13. Jing Wu & Kun Li & Jing Sun & Li Xie, 2018. "A Novel Integrated Method to Diagnose Faults in Power Transformers," Energies, MDPI, vol. 11(11), pages 1-8, November.
    14. 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.
    15. Javier Leiva & Rubén Carmona Pardo & José A. Aguado, 2019. "Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks," Energies, MDPI, vol. 12(7), pages 1-20, April.
    16. 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.
    17. 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.
    18. Bonginkosi A. Thango, 2022. "Application of the Analysis of Variance (ANOVA) in the Interpretation of Power Transformer Faults," Energies, MDPI, vol. 15(19), pages 1-17, October.
    19. Regelii Suassuna de Andrade Ferreira & Patrick Picher & Hassan Ezzaidi & Issouf Fofana, 2021. "A Machine-Learning Approach to Identify the Influence of Temperature on FRA Measurements," Energies, MDPI, vol. 14(18), pages 1-14, September.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8885-:d:982965. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.