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Application of Markov Model to Estimate Individual Condition Parameters for Transformers

Author

Listed:
  • Amran Mohd Selva

    (Centre for Electromagnetic & Lightning Protection (CELP), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Norhafiz Azis

    (Centre for Electromagnetic & Lightning Protection (CELP), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Institute of Advanced Technology (ITMA), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Muhammad Sharil Yahaya

    (Centre for Electromagnetic & Lightning Protection (CELP), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka 76100, Malaysia)

  • Mohd Zainal Abidin Ab Kadir

    (Centre for Electromagnetic & Lightning Protection (CELP), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Institute of Power Engineering (IPE), Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia)

  • Jasronita Jasni

    (Centre for Electromagnetic & Lightning Protection (CELP), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Young Zaidey Yang Ghazali

    (Distribution Division, Tenaga Nasional Berhad, Wisma TNB, Jalan Timur, Petaling Jaya 46200, Selangor, Malaysia)

  • Mohd Aizam Talib

    (TNB Research Sdn. Bhd., No.1, Lorong Ayer Itam, Kawasan Institut Penyelidikan, Kajang 43000, Selangor, Malaysia)

Abstract

This paper presents a study to estimate individual condition parameters of the transformer population based on Markov Model (MM). The condition parameters under study were hydrogen (H 2 ), methane (CH 4 ), acetylene (C 2 H 2 ), ethylene (C 2 H 4 ), ethane (C 2 H 6 ), carbon monoxide (CO), carbon dioxide (CO 2 ), dielectric breakdown voltage, interfacial tension, colour, acidity, water content, and 2-furfuraldehyde (2-FAL). First, the individual condition parameter of the transformer population was ranked and sorted based on recommended limits as per IEEE Std. C57. 104-2008 and IEEE Std. C57.106-2015. Next, the mean for each of the condition parameters was computed and the transition probabilities for each condition parameters were obtained based on non-linear optimization technique. Next, the future states probability distribution was computed based on the MM prediction model. Chi-square test and percentage of absolute error analysis were carried out to find the goodness-of-fit between predicted and computed condition parameters. It is found that estimation for majority of the individual condition parameter of the transformer population can be carried out by MM. The Chi-square test reveals that apart from CH 4 and C 2 H 4 , the condition parameters are outside the rejection region that indicates agreement between predicted and computed values. It is also observed that the lowest and highest percentages of differences between predicted and computed values of all the condition parameters are 81.46% and 98.52%, respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2114-:d:163632
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    References listed on IDEAS

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    1. Hazlee Azil Illias & Wee Zhao Liang, 2018. "Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-15, January.
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    Cited by:

    1. 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.
    2. 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.

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