IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i3p588-d486121.html
   My bibliography  Save this article

Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks

Author

Listed:
  • Ancuța-Mihaela Aciu

    (Research and Development Department, National Institute for Research, Development and Testing in Electrical Engineering-ICMET Craiova, 200746 Craiova, Romania)

  • Claudiu-Ionel Nicola

    (Research and Development Department, National Institute for Research, Development and Testing in Electrical Engineering-ICMET Craiova, 200746 Craiova, Romania
    Department of Automatic Control and Electronics, University of Craiova, 200585 Craiova, Romania)

  • Marcel Nicola

    (Research and Development Department, National Institute for Research, Development and Testing in Electrical Engineering-ICMET Craiova, 200746 Craiova, Romania)

  • Maria-Cristina Nițu

    (Research and Development Department, National Institute for Research, Development and Testing in Electrical Engineering-ICMET Craiova, 200746 Craiova, Romania
    Department Electrical Engineering, Energetic and Aeronautics, University of Craiova, 200585 Craiova, Romania)

Abstract

Power transformers play an important role in electrical systems; being considered the core of electric power transmissions and distribution networks, the owners and users of these assets are increasingly concerned with adopting reliable, automated, and non-invasive techniques to monitor and diagnose their operating conditions. Thus, monitoring the conditions of power transformers has evolved, in the sense that a complete characterization of the conditions of oil–paper insulation can be achieved through dissolved gas analysis (DGA) and furan compounds analysis, since these analyses provide a lot of information about the phenomena that occur in power transformers. The Duval triangles and pentagons methods can be used with a high percentage of correct predictions compared to the known classical methods (key gases, International Electrotechnical Commission (IEC), Rogers, Doernenburg ratios), because, in addition to the six types of basic faults, they also identify four sub-types of thermal faults that provide important additional information for the appropriate corrective actions to be applied to the transformers. A new approach is presented based on the complementarity between the analysis of the gases dissolved in the transformer oil and the analysis of furan compounds, for the identification of the different faults, especially when there are multiple faults, by extending the diagnosis of the operating conditions of the power transformers, in terms of paper degradation. The implemented software system based on artificial neural networks was tested and validated in practice, with good results.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:588-:d:486121
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/3/588/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/3/588/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmed Abu-Siada, 2019. "Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming," Energies, MDPI, vol. 12(4), pages 1-13, February.
    2. Bing Zeng & Jiang Guo & Wenqiang Zhu & Zhihuai Xiao & Fang Yuan & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM," Energies, MDPI, vol. 12(21), pages 1-18, November.
    3. Weigen Chen & Xi Chen & Shangyi Peng & Jian Li, 2012. "Canonical Correlation Between Partial Discharges and Gas Formation in Transformer Oil Paper Insulation," Energies, MDPI, vol. 5(4), pages 1-17, April.
    4. Michel Duval & Thomas Heizmann, 2020. "Identification of Stray Gassing of Inhibited and Uninhibited Mineral Oils in Transformers," Energies, MDPI, vol. 13(15), pages 1-9, July.
    5. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine," Energies, MDPI, vol. 12(5), pages 1-18, March.
    6. Luiz Cheim & Michel Duval & Saad Haider, 2020. "Combined Duval Pentagons: A Simplified Approach," Energies, MDPI, vol. 13(11), pages 1-12, June.
    7. Haikun Shang & Junyan Xu & Zitao Zheng & Bing Qi & Liwei Zhang, 2019. "A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory," Energies, MDPI, vol. 12(20), pages 1-22, October.
    8. 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.
    9. Rahman Azis Prasojo & Harry Gumilang & Suwarno & Nur Ulfa Maulidevi & Bambang Anggoro Soedjarno, 2020. "A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation," Energies, MDPI, vol. 13(4), pages 1-20, February.
    10. Lefeng Cheng & Tao Yu & Guoping Wang & Bo Yang & Lv Zhou, 2018. "Hot Spot Temperature and Grey Target Theory-Based Dynamic Modelling for Reliability Assessment of Transformer Oil-Paper Insulation Systems: A Practical Case Study," Energies, MDPI, vol. 11(1), pages 1-26, January.
    11. Wei Zhang & Xiaohui Yang & Yeheng Deng & Anyi Li, 2020. "An Inspired Machine-Learning Algorithm with a Hybrid Whale Optimization for Power Transformer PHM," Energies, MDPI, vol. 13(12), pages 1-17, 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. 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.
    2. 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.
    3. Firas B. Ismail & Maisarah Mazwan & Hussein Al-Faiz & Marayati Marsadek & Hasril Hasini & Ammar Al-Bazi & Young Zaidey Yang Ghazali, 2022. "An Offline and Online Approach to the OLTC Condition Monitoring: A Review," Energies, MDPI, vol. 15(17), pages 1-18, September.
    4. Ancuța-Mihaela Aciu & Sorin Enache & Maria-Cristina Nițu, 2024. "A Reviewed Turn at of Methods for Determining the Type of Fault in Power Transformers Based on Dissolved Gas Analysis," Energies, MDPI, vol. 17(10), pages 1-26, May.
    5. Felipe M. Laburú & Thales W. Cabral & Felippe V. Gomes & Eduardo R. de Lima & José C. S. S. Filho & Luís G. P. Meloni, 2024. "New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers," Energies, MDPI, vol. 17(12), pages 1-20, June.

    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. Janvier Sylvestre N’cho & Issouf Fofana, 2020. "Review of Fiber Optic Diagnostic Techniques for Power Transformers," Energies, MDPI, vol. 13(7), pages 1-24, April.
    2. Alper Aydogan & Fatih Atalar & Aysel Ersoy Yilmaz & Pawel Rozga, 2020. "Using the Method of Harmonic Distortion Analysis in Partial Discharge Assessment in Mineral Oil in a Non-Uniform Electric Field," Energies, MDPI, vol. 13(18), pages 1-18, September.
    3. Kai Ding & Chen Yao & Yifan Li & Qinglong Hao & Yaqiong Lv & Zengrui Huang, 2022. "A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System," Sustainability, MDPI, vol. 14(10), pages 1-28, May.
    4. George Kimani Irungu & Aloys Oriedi Akumu, 2020. "Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment," Energies, MDPI, vol. 13(18), pages 1-24, September.
    5. Bonginkosi A. Thango, 2022. "Dissolved Gas Analysis and Application of Artificial Intelligence Technique for Fault Diagnosis in Power Transformers: A South African Case Study," Energies, MDPI, vol. 15(23), pages 1-17, November.
    6. Michel Duval & Constantin Ene, 2021. "Identification of Stray Gassing of Dodecylbenzene in Bushings," Energies, MDPI, vol. 14(9), pages 1-7, April.
    7. 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.
    8. Yiyi Zhang & Yuxuan Wang & Xianhao Fan & Wei Zhang & Ran Zhuo & Jian Hao & Zhen Shi, 2020. "An Integrated Model for Transformer Fault Diagnosis to Improve Sample Classification near Decision Boundary of Support Vector Machine," Energies, MDPI, vol. 13(24), pages 1-15, December.
    9. Rahman Azis Prasojo & Harry Gumilang & Suwarno & Nur Ulfa Maulidevi & Bambang Anggoro Soedjarno, 2020. "A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation," Energies, MDPI, vol. 13(4), pages 1-20, February.
    10. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.
    11. Bing Zeng & Jiang Guo & Wenqiang Zhu & Zhihuai Xiao & Fang Yuan & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM," Energies, MDPI, vol. 12(21), pages 1-18, November.
    12. Wei Zhang & Xiaohui Yang & Yeheng Deng & Anyi Li, 2020. "An Inspired Machine-Learning Algorithm with a Hybrid Whale Optimization for Power Transformer PHM," Energies, MDPI, vol. 13(12), pages 1-17, June.
    13. Arputhasamy Joseph Amalanathan & Ramanujam Sarathi & Maciej Zdanowski & Ravikrishnan Vinu & Zbigniew Nadolny, 2023. "Review on Gassing Tendency of Different Insulating Fluids towards Transformer Applications," Energies, MDPI, vol. 16(1), pages 1-15, January.
    14. Yunhe Luo & Xiaosong Zou & Wei Xiong & Xufeng Yuan & Kui Xu & Yu Xin & Ruoyu Zhang, 2023. "Dynamic State Evaluation Method of Power Transformer Based on Mahalanobis–Taguchi System and Health Index," Energies, MDPI, vol. 16(6), pages 1-16, March.
    15. 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.
    16. Chenmeng Xiang & Quan Zhou & Jian Li & Qingdan Huang & Haoyong Song & Zhaotao Zhang, 2016. "Comparison of Dissolved Gases in Mineral and Vegetable Insulating Oils under Typical Electrical and Thermal Faults," Energies, MDPI, vol. 9(5), pages 1-22, April.
    17. Enwen Li & Linong Wang & Bin Song & Siliang Jian, 2018. "Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data," Energies, MDPI, vol. 11(9), pages 1-17, September.
    18. 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.
    19. Przemyslaw Goscinski & Zbigniew Nadolny & Andrzej Tomczewski & Ryszard Nawrowski & Tomasz Boczar, 2023. "The Influence of Heat Transfer Coefficient α of Insulating Liquids on Power Transformer Cooling Systems," Energies, MDPI, vol. 16(6), pages 1-15, March.
    20. Franciszek Witos & Aneta Olszewska, 2023. "Investigation of Partial Discharges within Power Oil Transformers by Acoustic Emission," Energies, MDPI, vol. 16(9), pages 1-20, April.

    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:3:p:588-:d:486121. 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.