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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 Management Strategies

Author

Listed:
  • Muhammad Sharil Yahaya

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

  • Norhafiz Azis

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

  • Amran Mohd Selva

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

  • Mohd Zainal Abidin Ab Kadir

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

  • Jasronita Jasni

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

  • Mohd Hendra Hairi

    (Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia)

  • Young Zaidey Yang Ghazali

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

  • Mohd Aizam Talib

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

Abstract

This paper presents an investigation of the condition state distribution and performance condition curve of the transformer population under different pre-determined maintenance repair rates based on the Markov Prediction Model (MPM). In total, 3195 oil samples from 373 transformers with an age between one and 25 years were tested. The previously computed Health Index (HI) prediction model of the transformer population based on MPM utilizing the nonlinear minimization technique was employed in this study. The transition probabilities for each of the states were updated based on 10%, 20% and 30% pre-determined maintenance repair rates for the sensitivity study. Next, the HI state distribution and performance condition curve were analyzed based on the Markov chain algorithm. Based on the case study, it is found that the pre-determined maintenance repair rates can affect the HI state distribution and improve the performance condition curve. The 30% pre-determined maintenance repair rate gives the highest impact, especially for the transformer population at state 4 (poor). Overall, the average percentage of change for all HI state distributions is 16.48%. A clear improvement of HI state distribution is found at state 4 (poor) where the highest percentage can be up to 63.25%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3399-:d:171760
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    References listed on IDEAS

    as
    1. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    2. Muhammad Sharil Yahaya & Norhafiz Azis & Mohd Zainal Abidin Ab Kadir & Jasronita Jasni & Mohd Hendra Hairi & Mohd Aizam Talib, 2017. "Estimation of Transformers Health Index Based on the Markov Chain," Energies, MDPI, vol. 10(11), pages 1-11, November.
    3. 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.
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    Cited by:

    1. Pedro J. Zarco-Periñán & José L. Martínez-Ramos & Fco. Javier Zarco-Soto, 2021. "On the Remuneration to Electrical Utilities and Budgetary Allocation for Substation Maintenance Management," Sustainability, MDPI, vol. 13(18), pages 1-15, September.
    2. Elizaveta Gavrikova & Irina Volkova & Yegor Burda, 2020. "Strategic Aspects of Asset Management: An Overview of Current Research," Sustainability, MDPI, vol. 12(15), pages 1-31, July.
    3. 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.
    4. 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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