IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i7p1789-d342714.html
   My bibliography  Save this article

Review of Fiber Optic Diagnostic Techniques for Power Transformers

Author

Listed:
  • Janvier Sylvestre N’cho

    (Département Génie Électrique et Électronique, Institut National Polytechnique Houphouët Boigny (INP-HB), BP 1093 Yamoussoukro, Cote D’Ivoire)

  • Issouf Fofana

    (Research Chair on the Aging of Power Network Infrastructure (ViAHT), Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada)

Abstract

Diagnostic and condition monitoring of power transformers are key actions to guarantee their safe operation. The subsequent benefits include reduced service interruptions and economic losses associated with their unavailability. Conventional test methods developed for the condition assessment of power transformers have certain limitations. To overcome such problems, fiber optic-based sensors for monitoring the condition of transformers have been developed. Flawlessly built-up fiber optic-based sensors provide online and offline assessment of various parameters like temperature, moisture, partial discharges, gas analyses, vibration, winding deformation, and oil levels, which are based on different sensing principles. In this paper a variety and assessment of different fiber optic-based diagnostic techniques for monitoring power transformers are discussed. It includes significant tutorial elements as well as some analyses.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1789-:d:342714
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/7/1789/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/7/1789/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmed Abu-Siada, 2019. "Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming," Energies, MDPI, vol. 12(4), pages 1-13, February.
    2. Jian Li & Xudong Li & Lin Du & Min Cao & Guochao Qian, 2016. "An Intelligent Sensor for the Ultra-High-Frequency Partial Discharge Online Monitoring of Power Transformers," Energies, MDPI, vol. 9(5), pages 1-15, May.
    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.
    4. 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.
    5. Haikun Shang & Junyan Xu & Zitao Zheng & Bing Qi & Liwei Zhang, 2019. "A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory," Energies, MDPI, vol. 12(20), pages 1-22, October.
    6. Issouf Fofana & Yazid Hadjadj, 2016. "Electrical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers," Energies, MDPI, vol. 9(9), pages 1-26, August.
    7. 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.
    8. Bing Zeng & Jiang Guo & Fangqing Zhang & Wenqiang Zhu & Zhihuai Xiao & Sixu Huang & Peng Fan, 2020. "Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition," Energies, MDPI, vol. 13(2), pages 1-20, January.
    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. Tian Tian & Xiu Zhou & Sihan Wang & Yan Luo & Xiuguang Li & Ninghui He & Yunlong Ma & Weifeng Liu & Rongbin Shi & Guoming Ma, 2022. "A π-Phase-Shifted Fiber Bragg Grating Partial Discharge Sensor toward Power Transformers," Energies, MDPI, vol. 15(16), pages 1-9, August.
    2. Veeresh Ramnarine & Vidyadhar Peesapati & Siniša Djurović, 2023. "Fibre Bragg Grating Sensors for Condition Monitoring of High-Voltage Assets: A Review," Energies, MDPI, vol. 16(18), pages 1-26, September.
    3. Iago Búa-Núñez & Julio E. Posada-Román & José A. García-Souto, 2021. "Multichannel Detection of Acoustic Emissions and Localization of the Source with External and Internal Sensors for Partial Discharge Monitoring of Power Transformers," Energies, MDPI, vol. 14(23), pages 1-20, November.

    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. Issouf Fofana & Yazid Hadjadj, 2018. "Power Transformer Diagnostics, Monitoring and Design Features," Energies, MDPI, vol. 11(12), pages 1-5, November.
    2. Wojciech Sikorski, 2018. "Active Dielectric Window: A New Concept of Combined Acoustic Emission and Electromagnetic Partial Discharge Detector for Power Transformers," Energies, MDPI, vol. 12(1), pages 1-27, December.
    3. Ancuța-Mihaela Aciu & Claudiu-Ionel Nicola & Marcel Nicola & Maria-Cristina Nițu, 2021. "Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks," Energies, MDPI, vol. 14(3), pages 1-22, 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. 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.
    6. Kai Ding & Chen Yao & Yifan Li & Qinglong Hao & Yaqiong Lv & Zengrui Huang, 2022. "A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System," Sustainability, MDPI, vol. 14(10), pages 1-28, May.
    7. 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.
    8. Andrew Adewunmi Adekunle & Samson Okikiola Oparanti & Issouf Fofana, 2023. "Performance Assessment of Cellulose Paper Impregnated in Nanofluid for Power Transformer Insulation Application: A Review," Energies, MDPI, vol. 16(4), pages 1-32, February.
    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. Chenmeng Zhang & Kailin Zhao & Shijun Xie & Can Hu & Yu Zhang & Nanxi Jiang, 2021. "Research on the Time-Domain Dielectric Response of Multiple Impulse Voltage Aging Oil-Film Dielectrics," Energies, MDPI, vol. 14(7), pages 1-15, April.
    11. Konrad Kierczynski & Przemyslaw Rogalski & Vitalii Bondariev & Pawel Okal & Daniel Korenciak, 2022. "Research on the Influence of Moisture Exchange between Oil and Cellulose on the Electrical Parameters of the Insulating Oil in Power Transformers," Energies, MDPI, vol. 15(20), pages 1-15, October.
    12. Przemyslaw Goscinski & Zbigniew Nadolny & Andrzej Tomczewski & Ryszard Nawrowski & Tomasz Boczar, 2023. "The Influence of Heat Transfer Coefficient α of Insulating Liquids on Power Transformer Cooling Systems," Energies, MDPI, vol. 16(6), pages 1-15, March.
    13. 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.
    14. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2020. "An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil," Energies, MDPI, vol. 13(7), pages 1-28, April.
    15. Amidou Betie & Fethi Meghnefi & Issouf Fofana & Zie Yeo, 2018. "Modeling the Insulation Paper Drying Process from Thermogravimetric Analyses," Energies, MDPI, vol. 11(3), pages 1-15, February.
    16. Pawel Zukowski & Przemyslaw Rogalski & Konrad Kierczynski & Tomasz N. Koltunowicz, 2021. "Precise Measurements of the Temperature Influence on the Complex Permittivity of Power Transformers Moistened Paper-Oil Insulation," Energies, MDPI, vol. 14(18), pages 1-24, September.
    17. 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.
    18. 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.
    19. Leila Safiddine & Hadj-Ziane Zafour & Ungarala Mohan Rao & Issouf Fofana, 2019. "Regeneration of Transformer Insulating Fluids Using Membrane Separation Technology," Energies, MDPI, vol. 12(3), pages 1-13, January.
    20. 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.

    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:13:y:2020:i:7:p:1789-:d:342714. 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.