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Review of Various Sensor Technologies in Monitoring the Condition of Power Transformers

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  • Meysam Beheshti Asl

    (Canada Research Chair Tier 1 in Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada)

  • Issouf Fofana

    (Canada Research Chair Tier 1 in Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada)

  • Fethi Meghnefi

    (Canada Research Chair Tier 1 in Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada)

Abstract

Modern power grids are undergoing a significant transformation with the massive integration of renewable, decentralized, and electronically interfaced energy sources, alongside new digital and wireless communication technologies. This transition necessitates the widespread adoption of robust online diagnostic and monitoring tools. Sensors, known for their intuitive and smart capabilities, play a crucial role in efficient condition monitoring, aiding in the prediction of power outages and facilitating the digital twinning of power equipment. This review comprehensively analyzes various sensor technologies used for monitoring power transformers, focusing on the critical need for reliable and efficient fault detection. The study explores the application of fiber Bragg grating (FBG) sensors, optical fiber sensors, wireless sensing networks, chemical sensors, ultra-high-frequency (UHF) sensors, and piezoelectric sensors in detecting parameters such as partial discharges, core condition, temperature, and dissolved gases. Through an extensive literature review, the sensitivity, accuracy, and practical implementation challenges of these sensor technologies are evaluated. Significant advances in real-time monitoring capabilities and improved diagnostic precision are highlighted in the review. It also identifies key challenges such as environmental susceptibility and the long-term stability of sensors. By synthesizing the current research and methodologies, this paper provides valuable insights into the integration and optimization of sensor technologies for enhancing transformer condition monitoring and reliability in modern power systems.

Suggested Citation

  • Meysam Beheshti Asl & Issouf Fofana & Fethi Meghnefi, 2024. "Review of Various Sensor Technologies in Monitoring the Condition of Power Transformers," Energies, MDPI, vol. 17(14), pages 1-40, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3533-:d:1437995
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    References listed on IDEAS

    as
    1. Laerty Damião & João Guimarães & Guilherme Ferraz & Edson Bortoni & Ronaldo Rossi & Renato Capelini & Rogério Salustiano & Estácio Tavares, 2020. "Online Monitoring of Partial Discharges in Power Transformers Using Capacitive Coupling in the Tap of Condenser Bushings," Energies, MDPI, vol. 13(17), pages 1-22, August.
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    Cited by:

    1. Xuemin Huang & Xiaoliang Zhuang & Fangyuan Tian & Zheng Niu & Yujie Chen & Qian Zhou & Chao Yuan, 2025. "A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction," Energies, MDPI, vol. 18(6), pages 1-22, March.
    2. Ivan Beloev & Mikhail Evgenievich Alpatov & Marsel Sharifyanovich Garifullin & Ilgiz Fanzilevich Galiev & Shamil Faridovich Rakhmankulov & Iliya Iliev & Ylia Sergeevna Valeeva, 2025. "Development of a Hybrid Expert Diagnostic System for Power Transformers Based on the Integration of Computational and Measurement Complexes," Energies, MDPI, vol. 18(20), pages 1-32, October.
    3. Meysam Beheshti Asl & Issouf Fofana & Fethi Meghnefi & Youssouf Brahami & Joao Pedro Da Costa Souza, 2025. "A Comprehensive Review of Transformer Winding Diagnostics: Integrating Frequency Response Analysis with Machine Learning Approaches," Energies, MDPI, vol. 18(5), pages 1-38, March.
    4. Jingrui Liu & Zhiwen Hou & Bowei Liu & Xinhui Zhou, 2025. "Mathematical and Machine Learning Innovations for Power Systems: Predicting Transformer Oil Temperature with Beluga Whale Optimization-Based Hybrid Neural Networks," Mathematics, MDPI, vol. 13(11), pages 1-34, May.
    5. Zhengyan Zhang & Yong Yi & Ji Qi & Qian Wang & Weiqi Qin & Xianhao Fan & Chuanyang Li, 2025. "Partial Discharge Characteristics of Typical Defects in Oil-Paper Insulation Based on Photon Detection Technology," Energies, MDPI, vol. 18(18), pages 1-13, September.

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