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Transformers Health Index Assessment Based on Neural-Fuzzy Network

Author

Listed:
  • Emran Jawad Kadim

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

  • Norhafiz Azis

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

  • Jasronita Jasni

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

  • Siti Anom Ahmad

    (Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Mohd Aizam Talib

    (TNB Research Sdn Bhd, Kajang 43000, Selangor, Malaysia)

Abstract

In this paper, an assessment on the health index (HI) of transformers is carried out based on Neural-Fuzzy (NF) method. In-service condition assessment data, such as dissolved gases, furans, AC breakdown voltage (ACBDV), moisture, acidity, dissipation factor (DF), color, interfacial tension (IFT), and age were fed as input parameters to the NF network. The NF network were trained individually based on two sets of data, known as in-service condition assessment and Monte Carlo Simulation (MCS) data. HI was also obtained from the scoring method for comparison with the NF method. It is found that the HI of transformers that was obtained by NF trained by MCS method is closer to scoring method than NF trained by in-service condition assessment method. Based on the total of 15 testing transformers, NF trained by MCS data method gives 10 transformers with the same assessments as scoring method as compared to eight transformers given by NF trained by in-service condition data method. Analysis based on all 73 transformers reveals that 62% of transformers have the same assessments between NF trained by MCS data and scoring methods.

Suggested Citation

  • Emran Jawad Kadim & Norhafiz Azis & Jasronita Jasni & Siti Anom Ahmad & Mohd Aizam Talib, 2018. "Transformers Health Index Assessment Based on Neural-Fuzzy Network," Energies, MDPI, vol. 11(4), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:710-:d:137431
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    Citations

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    Cited by:

    1. Muhammad Akmal A. Putra & Suwarno & Rahman Azis Prasojo, 2025. "Improving Transformer Health Index Prediction Performance Using Machine Learning Algorithms with a Synthetic Minority Oversampling Technique," Energies, MDPI, vol. 18(9), pages 1-44, May.
    2. 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.
    3. 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.
    4. Alhaytham Alqudsi & Ayman El-Hag, 2019. "Application of Machine Learning in Transformer Health Index Prediction," Energies, MDPI, vol. 12(14), pages 1-13, July.

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