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A Novel Condition Monitoring Procedure for Early Detection of Copper Corrosion Problems in Oil-Filled Electrical Transformers

Author

Listed:
  • Ramsey Jadim

    (Department of Mechanical Engineering, Faculty of Technology, Linnaeus University, 35195 Växjö, Sweden)

  • Mirka Kans

    (Department of Mechanical Engineering, Faculty of Technology, Linnaeus University, 35195 Växjö, Sweden)

  • Mohammed Alhattab

    (Primary Substation Maintenance Department, Ministry of Electricity and Water, Kuwait City 13001, Kuwait)

  • May Alhendi

    (Primary Substation Maintenance Department, Ministry of Electricity and Water, Kuwait City 13001, Kuwait)

Abstract

The negative impacts of catastrophic fire and explosion accidents due to copper corrosion problems of oil-filled electrical transformers are still in the spotlight due to a lack of effective methods for early fault detection. To address this gap, a condition monitoring (CM) procedure that can detect such problems in the initial stage is proposed in this paper. The suggested CM procedure is based on identified measurable variables, which are the relevant by-products of the corrosion reaction, and utilizes an Early Fault Diagnosis (EFD) model to detect and solve the copper corrosion problems. The EFD model includes a fault trend chart that can track a fault progression during the useful life of transformers. The purpose of this paper is to verify and validate the effectiveness of the suggested CM procedure by an empirical study in a power plant. The result of applying this procedure was early detection of copper corrosion problems in two transformers with suspected copper corrosion propagation from a total of 84. The corrective action was adding an optimized amount of a passivator, an anticorrosion additive, to suppress the corrosion reaction at the correct time. The main conclusion of this study is the importance of early detection of transformer faults to avoid the negative impacts on societal, company, and individual levels.

Suggested Citation

  • Ramsey Jadim & Mirka Kans & Mohammed Alhattab & May Alhendi, 2021. "A Novel Condition Monitoring Procedure for Early Detection of Copper Corrosion Problems in Oil-Filled Electrical Transformers," Energies, MDPI, vol. 14(14), pages 1-12, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4266-:d:594469
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    References listed on IDEAS

    as
    1. Amran Mohd Selva & Norhafiz Azis & Muhammad Sharil Yahaya & Mohd Zainal Abidin Ab Kadir & Jasronita Jasni & Young Zaidey Yang Ghazali & Mohd Aizam Talib, 2018. "Application of Markov Model to Estimate Individual Condition Parameters for Transformers," Energies, MDPI, vol. 11(8), pages 1-16, August.
    2. Niu, Gang & Yang, Bo-Suk & Pecht, Michael, 2010. "Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 786-796.
    3. Ramsey Jadim & Mirka Kans & Jesko Schulte & Mohammed Alhattab & May Alhendi & Ali Bushehry, 2021. "On Approaching Relevant Cost-Effective Sustainable Maintenance of Mineral Oil-Filled Electrical Transformers," Energies, MDPI, vol. 14(12), pages 1-17, June.
    4. Muhammad Sharil Yahaya & Norhafiz Azis & Amran Mohd Selva & Mohd Zainal Abidin Ab Kadir & Jasronita Jasni & Mohd Hendra Hairi & Young Zaidey Yang Ghazali & Mohd Aizam Talib, 2018. "Effect of Pre-Determined Maintenance Repair Rates on the Health Index State Distribution and Performance Condition Curve Based on the Markov Prediction Model for Sustainable Transformers Asset Managem," Sustainability, MDPI, vol. 10(10), pages 1-13, September.
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