IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i16p5213-d620159.html
   My bibliography  Save this article

The Analysis of Power Transformer Population Working in Different Operating Conditions with the Use of Health Index

Author

Listed:
  • Patryk Bohatyrewicz

    (Department of High Voltage and Power Engineering, Faculty of Electrical Engineering, West Pomeranian University of Technology, 70-313 Szczecin, Poland)

  • Andrzej Mrozik

    (Department of High Voltage and Power Engineering, Faculty of Electrical Engineering, West Pomeranian University of Technology, 70-313 Szczecin, Poland)

Abstract

The management of the power transformer population is a complex process, as the grid companies operate thousands of devices. For this issue, the health index method can be applied to facilitate asset management. The algorithm can be used not only in the technical assessment of the individual units, but also to determine the relationships within the whole population. In this paper, the presented health index method consists of periodic oil diagnostics, including the physicochemical properties, dissolved gas analysis, and furfural content, and further assessment in terms of the criticality of the device to determine the technical condition. The algorithm was specifically designed to reflect even the smallest changes of the input parameters in the final score. The performance of the health index was tested on 620 oil analyses from 220 transformers divided into four subpopulations based on the service conditions. The results have proven to be largely dependent on the criticality level and the operating conditions of the device. The analysis of the study group has shown the influence of corrective maintenance on the mean value of the health index score.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5213-:d:620159
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/16/5213/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/16/5213/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Tomasz Piotrowski & Pawel Rozga & Ryszard Kozak, 2019. "Comparative Analysis of the Results of Diagnostic Measurements with an Internal Inspection of Oil-Filled Power Transformers," Energies, MDPI, vol. 12(11), pages 1-18, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrey A. Radionov & Ivan V. Liubimov & Igor M. Yachikov & Ildar R. Abdulveleev & Ekaterina A. Khramshina & Alexander S. Karandaev, 2023. "Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data," Energies, MDPI, vol. 16(12), pages 1-27, June.
    2. Jonathan Velasco Costa & Diogo F. F. da Silva & Paulo J. Costa Branco, 2022. "Large-Power Transformers: Time Now for Addressing Their Monitoring and Failure Investigation Techniques," Energies, MDPI, vol. 15(13), pages 1-59, June.
    3. Inmaculada Fernández, 2022. "The Need for Experimental and Numerical Analyses of Thermal Ageing in Power Transformers," Energies, MDPI, vol. 15(17), pages 1-4, September.
    4. 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.
    5. Patryk Bohatyrewicz & Szymon Banaszak, 2022. "Assessment Criteria of Changes in Health Index Values over Time—A Transformer Population Study," Energies, MDPI, vol. 15(16), pages 1-15, August.
    6. Alexander S. Karandaev & Igor M. Yachikov & Andrey A. Radionov & Ivan V. Liubimov & Nikolay N. Druzhinin & Ekaterina A. Khramshina, 2022. "Fuzzy Algorithms for Diagnosis of Furnace Transformer Insulation Condition," Energies, MDPI, vol. 15(10), pages 1-21, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Fatih Atalar & Aysel Ersoy & Pawel Rozga, 2022. "Investigation of Effects of Different High Voltage Types on Dielectric Strength of Insulating Liquids," Energies, MDPI, vol. 15(21), pages 1-25, October.
    4. 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.
    5. Maciej Kuniewski, 2020. "FRA Diagnostics Measurement of Winding Deformation in Model Single-Phase Transformers Made with Silicon-Steel, Amorphous and Nanocrystalline Magnetic Cores," Energies, MDPI, vol. 13(10), pages 1-23, May.
    6. Michał Kozioł, 2020. "Energy Distribution of Optical Radiation Emitted by Electrical Discharges in Insulating Liquids," Energies, MDPI, vol. 13(9), pages 1-9, May.
    7. 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.
    8. 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.
    9. 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.
    10. Tomasz Piotrowski & Pawel Rozga & Ryszard Kozak & Zbigniew Szymanski, 2020. "Using the Analysis of the Gases Dissolved in Oil in Diagnosis of Transformer Bushings with Paper-Oil Insulation—A Case Study," Energies, MDPI, vol. 13(24), pages 1-12, December.
    11. Ancuța-Mihaela Aciu & Claudiu-Ionel Nicola & Marcel Nicola & Maria-Cristina Nițu, 2021. "Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks," Energies, MDPI, vol. 14(3), pages 1-22, January.
    12. Pawel Zukowski & Przemyslaw Rogalski & Tomasz N. Koltunowicz & Konrad Kierczynski & Vitalii Bondariev, 2020. "Precise Measurements of the Temperature-Frequency Dependence of the Conductivity of Cellulose—Insulating Oil—Water Nanoparticles Composite," Energies, MDPI, vol. 14(1), pages 1-26, December.
    13. Eugeniusz Kornatowski & Szymon Banaszak, 2019. "Frequency Response Quality Index for Assessing the Mechanical Condition of Transformer Windings," Energies, MDPI, vol. 13(1), pages 1-15, December.
    14. Alhaytham Alqudsi & Ayman El-Hag, 2019. "Application of Machine Learning in Transformer Health Index Prediction," Energies, MDPI, vol. 12(14), pages 1-13, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5213-:d:620159. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.