IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i4p716-d137480.html
   My bibliography  Save this article

A Non-Destructive Optical Method for the DP Measurement of Paper Insulation Based on the Free Fibers in Transformer Oil

Author

Listed:
  • Lei Peng

    (Electric science and research institute of Guangdong Power Grid Co. Ltd., Guangzhou 510080, China)

  • Qiang Fu

    (Electric science and research institute of Guangdong Power Grid Co. Ltd., Guangzhou 510080, China)

  • Yaohong Zhao

    (Electric science and research institute of Guangdong Power Grid Co. Ltd., Guangzhou 510080, China)

  • Yihua Qian

    (Electric science and research institute of Guangdong Power Grid Co. Ltd., Guangzhou 510080, China)

  • Tiansheng Chen

    (Electric science and research institute of Guangdong Power Grid Co. Ltd., Guangzhou 510080, China)

  • Shengping Fan

    (Electric science and research institute of Guangdong Power Grid Co. Ltd., Guangzhou 510080, China)

Abstract

In order to explore a non-destructive method for measuring the polymerization degree (DP) of paper insulation in transformer, a new method that based on the optical properties of free fiber particles in transformer oil was studied. The chromatic dispersion images of fibers with different aging degree were obtained by polarizing microscope, and the eigenvalues ( r , b , and Mahalanobis distance) of the images were extracted by the RGB (red, blue, and green) tricolor analysis method. Then, the correlation between the three eigenvalues and DP of paper insulation were simulated respectively. The results showed that the color of images changed from blue-purple to orange-yellow gradually with the increase of aging degree. For the three eigenvalues, the relationship between Mahalanobis distance and DP had the best goodness of fit (R 2 = 0.98), higher than that of r (0.94) and b (0.94). The mean square error of the relationship between Mahalanobis distance and DP (52.17) was also significantly lower than that of r and b (97.58, 98.05). Therefore, the DP of unknown paper insulation could be calculated by the simulated relationship of Mahalanobis distance and DP.

Suggested Citation

  • Lei Peng & Qiang Fu & Yaohong Zhao & Yihua Qian & Tiansheng Chen & Shengping Fan, 2018. "A Non-Destructive Optical Method for the DP Measurement of Paper Insulation Based on the Free Fibers in Transformer Oil," Energies, MDPI, vol. 11(4), pages 1-9, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:716-:d:137480
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/4/716/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/4/716/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Jingxin Zou & Weigen Chen & Fu Wan & Zhou Fan & Lingling Du, 2016. "Raman Spectral Characteristics of Oil-Paper Insulation and Its Application to Ageing Stage Assessment of Oil-Immersed Transformers," Energies, MDPI, vol. 9(11), pages 1-14, November.
    3. Janvier Sylvestre N’cho & Issouf Fofana & Yazid Hadjadj & Abderrahmane Beroual, 2016. "Review of Physicochemical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers," Energies, MDPI, vol. 9(5), pages 1-29, May.
    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. Fabio Henrique Pereira & Francisco Elânio Bezerra & Shigueru Junior & Josemir Santos & Ivan Chabu & Gilberto Francisco Martha de Souza & Fábio Micerino & Silvio Ikuyo Nabeta, 2018. "Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations," Energies, MDPI, vol. 11(7), pages 1-12, June.
    2. Xiaowen Wu & Ling Li & Nianguang Zhou & Ling Lu & Sheng Hu & Hao Cao & Zhiqiang He, 2018. "Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method," Energies, MDPI, vol. 11(7), pages 1-20, July.
    3. Hao Pan & Liang Xue & Chuankai Yang & Fenghong Chu & Youhua Jiang & Hongmei Zhu & Yue Li & Lei Xin, 2022. "Detection of Cellulose Particles in Transformer Oil Based on Transport of Intensity Equation," Energies, MDPI, vol. 15(16), pages 1-11, August.
    4. Piotr Przybylek & Hubert Moranda & Hanna Moscicka-Grzesiak & Dominika Szczesniak, 2019. "Application of Synthetic Ester for Drying Distribution Transformer Insulation—The Influence of Cellulose Thickness on Drying Efficiency," Energies, MDPI, vol. 12(20), pages 1-16, October.
    5. Kakou D. Kouassi & Issouf Fofana & Ladji Cissé & Yazid Hadjadj & Kouba M. Lucia Yapi & K. Ambroise Diby, 2018. "Impact of Low Molecular Weight Acids on Oil Impregnated Paper Insulation Degradation," Energies, MDPI, vol. 11(6), pages 1-13, June.
    6. Piotr Przybylek, 2018. "A New Concept of Applying Methanol to Dry Cellulose Insulation at the Stage of Manufacturing a Transformer," Energies, MDPI, vol. 11(7), pages 1-13, June.

    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. 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.
    2. Issouf Fofana & Yazid Hadjadj, 2018. "Power Transformer Diagnostics, Monitoring and Design Features," Energies, MDPI, vol. 11(12), pages 1-5, November.
    3. Sergio Bustamante & Mario Manana & Alberto Arroyo & Raquel Martinez & Alberto Laso, 2020. "A Methodology for the Calculation of Typical Gas Concentration Values and Sampling Intervals in the Power Transformers of a Distribution System Operator," Energies, MDPI, vol. 13(22), pages 1-18, November.
    4. Tomasz Piotrowski & Pawel Rozga & Ryszard Kozak, 2019. "Comparative Analysis of the Results of Diagnostic Measurements with an Internal Inspection of Oil-Filled Power Transformers," Energies, MDPI, vol. 12(11), pages 1-18, June.
    5. 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.
    6. Guoqiang Xia & Guangning Wu & Bo Gao & Haojie Yin & Feibao Yang, 2017. "A New Method for Evaluating Moisture Content and Aging Degree of Transformer Oil-Paper Insulation Based on Frequency Domain Spectroscopy," Energies, MDPI, vol. 10(8), pages 1-15, August.
    7. 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.
    8. Michał Kunicki & Sebastian Borucki & Andrzej Cichoń & Jerzy Frymus, 2019. "Modeling of the Winding Hot-Spot Temperature in Power Transformers: Case Study of the Low-Loaded Fleet," Energies, MDPI, vol. 12(18), pages 1-17, September.
    9. Piotr Przybylek & Hubert Moranda & Hanna Moscicka-Grzesiak & Dominika Szczesniak, 2019. "Application of Synthetic Ester for Drying Distribution Transformer Insulation—The Influence of Cellulose Thickness on Drying Efficiency," Energies, MDPI, vol. 12(20), pages 1-16, October.
    10. Wojciech Sikorski & Krzysztof Walczak & Piotr Przybylek, 2016. "Moisture Migration in an Oil-Paper Insulation System in Relation to Online Partial Discharge Monitoring of Power Transformers," Energies, MDPI, vol. 9(12), pages 1-16, December.
    11. 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.
    12. Jiangjun Ruan & Shuo Jin & Zhiye Du & Yiming Xie & Lin Zhu & Yu Tian & Ruohan Gong & Guannan Li & Min Xiong, 2017. "Condition Assessment of Paper Insulation in Oil-Immersed Power Transformers Based on the Iterative Inversion of Resistivity," Energies, MDPI, vol. 10(4), pages 1-15, April.
    13. 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.
    14. 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.
    15. 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.
    16. Janvier Sylvestre N’cho & Issouf Fofana, 2020. "Review of Fiber Optic Diagnostic Techniques for Power Transformers," Energies, MDPI, vol. 13(7), pages 1-24, April.
    17. 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.
    18. Arputhasamy Joseph Amalanathan & Ramanujam Sarathi & Maciej Zdanowski & Ravikrishnan Vinu & Zbigniew Nadolny, 2023. "Review on Gassing Tendency of Different Insulating Fluids towards Transformer Applications," Energies, MDPI, vol. 16(1), pages 1-15, January.
    19. Maciej Zdanowski, 2022. "Streaming Electrification of C 60 Fullerene Doped Insulating Liquids for Power Transformers Applications," Energies, MDPI, vol. 15(7), pages 1-14, March.
    20. Siti Rosilah Arsad & Pin Jern Ker & Md. Zaini Jamaludin & Pooi Ying Choong & Hui Jing Lee & Vimal Angela Thiviyanathan & Young Zaidey Yang Ghazali, 2023. "Water Content in Transformer Insulation System: A Review on the Detection and Quantification Methods," Energies, MDPI, vol. 16(4), pages 1-31, February.

    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:11:y:2018:i:4:p:716-:d:137480. 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.