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Fuzzy Algorithms for Diagnosis of Furnace Transformer Insulation Condition

Author

Listed:
  • Alexander S. Karandaev

    (Department of Information-Measuring Equipment, South Ural State University, 454080 Chelyabinsk, Russia)

  • Igor M. Yachikov

    (Department of Information-Measuring Equipment, South Ural State University, 454080 Chelyabinsk, Russia)

  • Andrey A. Radionov

    (Department of Automation and Control, Moscow Polytechnic University, 107023 Moscow, Russia)

  • Ivan V. Liubimov

    (Department of Electric Drive and Mechatronics, South Ural State University, 454080 Chelyabinsk, Russia)

  • Nikolay N. Druzhinin

    (Department of Electric Drive and Mechatronics, South Ural State University, 454080 Chelyabinsk, Russia)

  • Ekaterina A. Khramshina

    (Power Engineering and Automated Systems Institute, Nosov Magnitogorsk State Technical University, 455000 Magnitogorsk, Russia)

Abstract

Implementation of the smart transformer concept is critical for the deployment of IIoT-based smart grids. Top manufacturers of power electrics develop and adopt online monitoring systems. Such systems become part of high-voltage grid and unit transformers. However, furnace transformers are a broad category that this change does not affect yet. At the same time, adoption of diagnostic systems for furnace transformers is relevant because they are a heavy-duty application with no redundancy. Creating any such system requires a well-founded mathematical analysis of the facility’s condition, carefully selected diagnostic parameters, and setpoints thereof, which serve as the condition categories. The goal hereof was to create an expert system to detect insulation breach and its expansion as well as to evaluate the risk it poses to the system; the core mechanism is mathematical processing of trends in partial discharge ( PD ). We ran tests on a 26-MVA transformer installed on a ladle furnace at a steelworks facility. The transformer is equipped with a versatile condition monitoring system that continually measures apparent charge and PD intensity. The objective is to identify the condition of the transformer and label it with one of the generally recognized categories: Normal, Poor, Critical. The contribution of this paper consists of the first ever validation of a single generalized metric that describes the condition of transformer insulation based on the online monitoring of the PD parameters. Fuzzy logic algorithms are used in mathematical processing. The proposal is to generalize the set of diagnostic variables to a single deterministic parameter: insulation state indicator. The paper provides an example of calculating it from the apparent charge and PD power readings. To measure the indicativeness of individual parameters for predicting further development of a defect, the authors developed a method for testing the diagnostic sensitivity of these parameters to changes in the condition. The method was tested using trends in readings sampled whilst the status was degrading from Normal to Critical. The paper also shows a practical example of defect localization. The recommendation is to broadly use the method in expert systems for high-voltage equipment monitoring.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3519-:d:813277
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    References listed on IDEAS

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    1. Stefan Tenbohlen & Sebastian Coenen & Mohammad Djamali & Andreas Müller & Mohammad Hamed Samimi & Martin Siegel, 2016. "Diagnostic Measurements for Power Transformers," Energies, MDPI, vol. 9(5), pages 1-25, May.
    2. Tiago Rabelo Chaves & Marcos Aurélio Izumida Martins & Kennedy Alves Martins & Amadeu Fernandes de Macedo & Silvia de Francisci, 2021. "Application Study in the Field of Solutions for the Monitoring Distribution Transformers of the Overhead Power Grid," Energies, MDPI, vol. 14(19), pages 1-15, September.
    3. Olga Melnikova & Alexandr Nazarychev & Konstantin Suslov, 2022. "Enhancement of the Technique for Calculation and Assessment of the Condition of Major Insulation of Power Transformers," Energies, MDPI, vol. 15(4), pages 1-13, February.
    4. Vahid Behjat & Reza Emadifar & Mehrdad Pourhossein & U. Mohan Rao & Issouf Fofana & Reza Najjar, 2021. "Improved Monitoring and Diagnosis of Transformer Solid Insulation Using Pertinent Chemical Indicators," Energies, MDPI, vol. 14(13), pages 1-13, July.
    5. Christian Gianoglio & Edoardo Ragusa & Paolo Gastaldo & Federico Gallesi & Francesco Guastavino, 2021. "Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks," Energies, MDPI, vol. 14(15), pages 1-23, August.
    6. Alhaytham Alqudsi & Ayman El-Hag, 2019. "Application of Machine Learning in Transformer Health Index Prediction," Energies, MDPI, vol. 12(14), pages 1-13, July.
    7. Yuanlin Luo & Zhaohui Li & Hong Wang, 2017. "A Review of Online Partial Discharge Measurement of Large Generators," Energies, MDPI, vol. 10(11), pages 1-32, October.
    8. 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.
    9. Marek Florkowski, 2020. "Influence of Insulating Material Properties on Partial Discharges at DC Voltage," Energies, MDPI, vol. 13(17), pages 1-17, August.
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    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.

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