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Evolution of transformer health index in the form of mathematical equation

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  • Azmi, A.
  • Jasni, J.
  • Azis, N.
  • Kadir, M.Z.A. Ab.

Abstract

Energy is a basic necessity in every country. The worldwide demand for energy will rise due to the developments of power generation in industrial, service, and residential sectors. A healthy power system is therefore very important to guarantee continuous electricity supply to the end users and this can be achieved through asset management. A proper asset management will allow asset managers to conduct quality assessment of conditions and to develop future management strategies of the electrical assets such as transformers. The execution of transformer asset management involves an investigation of the transformer's condition by employing Transformer's Health Index (THI). Mathematical equation/algorithm or expert judgment has been investigated by many previous studies as one of the technique to determine health index (HI). Some of the established methods of HI determination such as scoring and ranking method, tier method, matrices and multi-feature assessment model have led to the different interpretations of the final condition of a transformer. This paper critically examines and explores the previous studies related to transformer health index by using mathematical equation/algorithm or expert judgment. The concept of HI and its formulation are presented in this study. Generally, there are three parts of HI formulation which are input, algorithm for HI and the output of HI. The application of HI is discussed in terms of the performance of in-service transformer. The limitations of the available methods are also discussed and future works to overcome the problems are suggested.

Suggested Citation

  • Azmi, A. & Jasni, J. & Azis, N. & Kadir, M.Z.A. Ab., 2017. "Evolution of transformer health index in the form of mathematical equation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 687-700.
    • Handle: RePEc:eee:rensus:v:76:y:2017:i:c:p:687-700
    DOI: 10.1016/j.rser.2017.03.094
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    References listed on IDEAS

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    Cited by:

    1. Olga Melnikova & Alexandr Nazarychev & Konstantin Suslov, 2022. "Enhancement of the Technique for Calculation and Assessment of the Condition of Major Insulation of Power Transformers," Energies, MDPI, vol. 15(4), pages 1-13, February.
    2. Raji Murugan & Ramasamy Raju, 2021. "Evaluation of in-service power transformer health condition for Inspection, Repair, and Replacement (IRR) maintenance planning in electric utilities," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(2), pages 318-336, April.
    3. Bustamante, Sergio & Manana, Mario & Arroyo, Alberto & Laso, Alberto & Martinez, Raquel, 2024. "Evolution of graphical methods for the identification of insulation faults in oil-immersed power transformers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    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.
    5. 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.
    6. Alhaytham Alqudsi & Ayman El-Hag, 2019. "Application of Machine Learning in Transformer Health Index Prediction," Energies, MDPI, vol. 12(14), pages 1-13, July.
    7. David L. Alvarez & Diego F. Rodriguez & Alben Cardenas & F. Faria da Silva & Claus Leth Bak & Rodolfo García & Sergio Rivera, 2021. "Optimal Decision Making in Electrical Systems Using an Asset Risk Management Framework," Energies, MDPI, vol. 14(16), pages 1-25, August.

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