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Review of Fiber Optic Diagnostic Techniques for Power Transformers

Author

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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.
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    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.

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