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Applied Complex Diagnostics and Monitoring of Special Power Transformers

Author

Listed:
  • Georgi Ivanov

    (Centralna Energoremontna Baza EAD, Cerb TRAFO, Lokomotiv 1, 1220 Sofia, Bulgaria
    Department of Electrical Apparatus, Technical University of Sofia, 1797 Sofia, Bulgaria)

  • Anelia Spasova

    (Centralna Himicheska Laboratoria Ltd., Lokomotiv 1, 1220 Sofia, Bulgaria)

  • Valentin Mateev

    (Department of Electrical Apparatus, Technical University of Sofia, 1797 Sofia, Bulgaria)

  • Iliana Marinova

    (Department of Electrical Apparatus, Technical University of Sofia, 1797 Sofia, Bulgaria)

Abstract

As a major component in electric power systems, power transformers are one of the most expensive and important pieces of electrical equipment. The trouble-free operation of power transformers is an important criterion for safety and stability in a power system. Technical diagnostics of electrical equipment are a mandatory part of preventing accidents and ensuring the continuity of the power supply. In this study, a complex diagnostic methodology was proposed and applied for special power transformers’ risk estimation. Twenty special power transformers were scored with the proposed risk estimation methodology. For each transformer, dissolved gas analysis (DGA) tests, transformer oil quality analysis, visual inspections of all current equipment on-site and historical data for the operation of each electrical research were conducted. All data were collected and analyzed under historical records of malfunctioning events. Statistical data for expected fault risk, based on long-term records, with such types of transformers were used to make more precise estimations of the current state of each machine and expected operational resource. The calculated degree of insulation polymerization was made via an ANN-assisted predictive method. Assessment of the collected data was applied to allow detailed information of the state of the power transformer to be rated. A method for risk assessment and reliability estimation was proposed and applied, based on the health index (HI) for each transformer.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2142-:d:1077236
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    References listed on IDEAS

    as
    1. Dimitris A. Barkas & Stavros D. Kaminaris & Konstantinos K. Kalkanis & George Ch. Ioannidis & Constantinos S. Psomopoulos, 2022. "Condition Assessment of Power Transformers through DGA Measurements Evaluation Using Adaptive Algorithms and Deep Learning," Energies, MDPI, vol. 16(1), pages 1-17, December.
    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 & 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.
    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.
    5. Bonginkosi A. Thango & Pitshou N. Bokoro, 2022. "Prediction of the Degree of Polymerization in Transformer Cellulose Insulation Using the Feedforward Backpropagation Artificial Neural Network," Energies, MDPI, vol. 15(12), pages 1-12, June.
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    Cited by:

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    2. Mohammad Amin Faraji & Alireza Shooshtari & Ayman El-Hag, 2023. "Stacked Ensemble Regression Model for Prediction of Furan," Energies, MDPI, vol. 16(22), pages 1-11, November.

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