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Estimation of Transformers Health Index Based on the Markov Chain

Author

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  • Muhammad Sharil Yahaya

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

  • Norhafiz Azis

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

  • Mohd Zainal Abidin Ab Kadir

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

  • Jasronita Jasni

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

  • Mohd Hendra Hairi

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

  • Mohd Aizam Talib

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

Abstract

This paper presents a study on the application of the Markov Model (MM) to determine the transformer population states based on Health Index (HI). In total, 3195 oil samples from 373 transformers ranging in age from 1 to 25 years were analyzed. First, the HI of transformers was computed based on yearly individual oil condition monitoring data that consisted of oil quality, dissolved gases, and furanic compounds. Next, the average HI for each age was computed and the transition probabilities were obtained based on a nonlinear optimization technique. Finally, the future deterioration performance curve of the transformers was determined based on the MM chain algorithm. It was found that the MM can be used to predict the future transformers condition states. The chi-squared goodness-of-fit analysis revealed that the predicted HI for the transformer population obtained based on MM agrees with the average computed HI along the years, and the average error is 3.59%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1824-:d:118286
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    References listed on IDEAS

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    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. Davis, Wayne J & Carnahan, James V, 1987. "Decision support for road surface maintenance," Omega, Elsevier, vol. 15(4), pages 313-322.
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    Cited by:

    1. Muhammad Sharil Yahaya & Norhafiz Azis & Amran Mohd Selva & Mohd Zainal Abidin Ab Kadir & Jasronita Jasni & Emran Jawad Kadim & Mohd Hendra Hairi & Young Zaidey Yang Ghazali, 2018. "A Maintenance Cost Study of Transformers Based on Markov Model Utilizing Frequency of Transition Approach," Energies, MDPI, vol. 11(8), pages 1-14, August.
    2. Patryk Bohatyrewicz & Andrzej Mrozik, 2021. "The Analysis of Power Transformer Population Working in Different Operating Conditions with the Use of Health Index," Energies, MDPI, vol. 14(16), pages 1-14, August.
    3. 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.
    4. Nguyen Thanh Viet & Alla G. Kravets, 2022. "The New Method for Analyzing Technology Trends of Smart Energy Asset Performance Management," Energies, MDPI, vol. 15(18), pages 1-26, September.
    5. Georgi Ivanov & Anelia Spasova & Valentin Mateev & Iliana Marinova, 2023. "Applied Complex Diagnostics and Monitoring of Special Power Transformers," Energies, MDPI, vol. 16(5), pages 1-24, February.
    6. Oussama Laayati & Hicham El Hadraoui & Adila El Magharaoui & Nabil El-Bazi & Mostafa Bouzi & Ahmed Chebak & Josep M. Guerrero, 2022. "An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems," Energies, MDPI, vol. 15(19), pages 1-28, October.
    7. Hyeseon Lee & Byungsung Lee & Gyurim Han & Yuri Kim & Yongha Kim, 2023. "Development of Methods for an Overhead Cable Health Index Evaluation That Considers Economic Feasibility," Energies, MDPI, vol. 16(20), pages 1-13, October.

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