IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i11p1842-d118444.html
   My bibliography  Save this article

Study on Quantitative Correlations between the Ageing Condition of Transformer Cellulose Insulation and the Large Time Constant Obtained from the Extended Debye Model

Author

Listed:
  • Yiyi Zhang

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
    National Demonstration Center for Experimental Electrical Engineering Education, Guangxi University, Nanning 530004, China
    These authors contributed equally to this work.)

  • Jiefeng Liu

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
    Shijiazhuang Power Supply Branch of State Grid Electric Power Company, Shijiazhuang 050000, China
    These authors contributed equally to this work.)

  • Hanbo Zheng

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
    State Grid Henan Electric Power Research Institute, Zhengzhou 450052, China
    These authors contributed equally to this work.)

  • Hua Wei

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China)

  • Ruijin Liao

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

Abstract

Polarization-depolarization current (PDC) measurements are now being used as a diagnosis tool to predict the ageing condition of transformer oil-paper insulation. Unfortunately, it is somewhat difficult to obtain the ageing condition of transformer cellulose insulation using the PDC technique due to the variation in transformer insulation geometry. In this literature, to quantify the ageing condition of transformer cellulose insulation using the PDC technique, we firstly designed a series of experiments under controlled laboratory conditions, and then obtained the branch parameters of an extended Debye model using the technique of curve fitting the PDC data. Finally, the ageing effect and water effect on the parameters of large time constant branches were systematically investigated. In the present paper, it is observed that there is a good exponential correlation between large time constants and degree of polymerization (DP). Therefore, the authors believe that the large time constants may be regard as a sensitive ageing indicator and the nice correlations might be utilized for the quantitative assessment of ageing condition in transformer cellulose insulation in the future due to the geometry independence of large time constants. In addition, it is found that the water in cellulose pressboards has a predominant effect on large time constants.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1842-:d:118444
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/11/1842/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/11/1842/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yazid Hadjadj & Fethi Meghnefi & Issouf Fofana & Hassan Ezzaidi, 2013. "On the Feasibility of Using Poles Computed from Frequency Domain Spectroscopy to Assess Oil Impregnated Paper Insulation Conditions," Energies, MDPI, vol. 6(4), pages 1-17, April.
    2. Stefan Tenbohlen & Sebastian Coenen & Mohammad Djamali & Andreas Müller & Mohammad Hamed Samimi & Martin Siegel, 2016. "Diagnostic Measurements for Power Transformers," Energies, MDPI, vol. 9(5), pages 1-25, May.
    3. 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.
    4. 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.
    5. 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.
    6. Rahman A. Prasojo & Karunika Diwyacitta & Suwarno & Harry Gumilang, 2017. "Transformer Paper Expected Life Estimation Using ANFIS Based on Oil Characteristics and Dissolved Gases (Case Study: Indonesian Transformers)," Energies, MDPI, vol. 10(8), pages 1-18, August.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Minghui Ou & Hua Wei & Yiyi Zhang & Jiancheng Tan, 2019. "A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers," Energies, MDPI, vol. 12(6), pages 1-16, March.
    2. Nuria Novas & Alfredo Alcayde & Isabel Robalo & Francisco Manzano-Agugliaro & Francisco G. Montoya, 2020. "Energies and Its Worldwide Research," Energies, MDPI, vol. 13(24), pages 1-41, December.
    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. Hanbo Zheng & Jiefeng Liu & Yiyi Zhang & Yijie Ma & Yang Shen & Xiaochen Zhen & Zilai Chen, 2018. "Effectiveness Analysis and Temperature Effect Mechanism on Chemical and Electrical-Based Transformer Insulation Diagnostic Parameters Obtained from PDC Data," Energies, MDPI, vol. 11(1), pages 1-17, January.
    5. Jiake Fang & Hanbo Zheng & Jiefeng Liu & Junhui Zhao & Yiyi Zhang & Ke Wang, 2018. "A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier," Energies, MDPI, vol. 11(8), pages 1-18, July.
    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. 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.
    8. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine," Energies, MDPI, vol. 12(5), pages 1-18, March.
    9. 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.
    10. Jedsada Raxsa & Phethai Nimsanong & Thanatorn Mai-Eiam & Peerawut Yutthagowith, 2023. "Determination of Dielectric Models Based on Effective Multi-Exponential Fittings," Energies, MDPI, vol. 16(12), pages 1-21, June.
    11. Zhilin Lyu & Qing Wei & Yiyi Zhang & Junhui Zhao & Emad Manla, 2018. "Adaptive Virtual Impedance Droop Control Based on Consensus Control of Reactive Current," Energies, MDPI, vol. 11(7), pages 1-17, July.
    12. Fenglan Tian & Zhongzhao Jing & Huan Zhao & Enze Zhang & Jiefeng Liu, 2019. "A Synthetic Condition Assessment Model for Power Transformers Using the Fuzzy Evidence Fusion Method," Energies, MDPI, vol. 12(5), pages 1-17, March.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Hanbo Zheng & Jiefeng Liu & Yiyi Zhang & Yijie Ma & Yang Shen & Xiaochen Zhen & Zilai Chen, 2018. "Effectiveness Analysis and Temperature Effect Mechanism on Chemical and Electrical-Based Transformer Insulation Diagnostic Parameters Obtained from PDC Data," Energies, MDPI, vol. 11(1), pages 1-17, January.
    9. Jiake Fang & Hanbo Zheng & Jiefeng Liu & Junhui Zhao & Yiyi Zhang & Ke Wang, 2018. "A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier," Energies, MDPI, vol. 11(8), pages 1-18, July.
    10. 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.
    11. Nuria Novas & Alfredo Alcayde & Isabel Robalo & Francisco Manzano-Agugliaro & Francisco G. Montoya, 2020. "Energies and Its Worldwide Research," Energies, MDPI, vol. 13(24), pages 1-41, December.
    12. 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.
    13. 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.
    14. Miro Antonijević & Stjepan Sučić & Hrvoje Keserica, 2018. "Augmented Reality Applications for Substation Management by Utilizing Standards-Compliant SCADA Communication," Energies, MDPI, vol. 11(3), pages 1-17, March.
    15. 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.
    16. Yulong Wang & Xiaohong Zhang & Lili Li & Jinyang Du & Junguo Gao, 2019. "Design of Partial Discharge Test Environment for Oil-Filled Submarine Cable Terminals and Ultrasonic Monitoring," Energies, MDPI, vol. 12(24), pages 1-14, December.
    17. Issouf Fofana & Yazid Hadjadj, 2018. "Power Transformer Diagnostics, Monitoring and Design Features," Energies, MDPI, vol. 11(12), pages 1-5, November.
    18. Szymon Banaszak & Konstanty Marek Gawrylczyk & Katarzyna Trela, 2020. "Frequency Response Modelling of Transformer Windings Connected in Parallel," Energies, MDPI, vol. 13(6), pages 1-13, March.
    19. Szymon Banaszak & Wojciech Szoka, 2018. "Cross Test Comparison in Transformer Windings Frequency Response Analysis," Energies, MDPI, vol. 11(6), pages 1-12, May.
    20. Fatih Atalar & Aysel Ersoy & Pawel Rozga, 2022. "Investigation of Effects of Different High Voltage Types on Dielectric Strength of Insulating Liquids," Energies, MDPI, vol. 15(21), pages 1-25, October.

    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:10:y:2017:i:11:p:1842-:d:118444. 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.