IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1168-d1042809.html
   My bibliography  Save this article

Transformer Fault Early Warning Analysis Based on Hierarchical Clustering Combined with Decision Trees

Author

Listed:
  • Xiaoqiang Liu

    (School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China)

  • Ji Li

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Lei Shao

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Hongli Liu

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Lei Ren

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Lihua Zhu

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

Abstract

The issues of low accuracy, poor generality, high cost of transformer fault early warning, and the subjective nature of empirical judgments made by field maintenance personnel are difficult to solve with the traditional measurement methods used during the development of the transformer. To construct a transformer fault early warning analysis, this study recommends a data-fusion-based decision tree approach for merging electrical quantity signals with a non-electrical amount of vibration signals. By merging a decision tree inference with actual operation data, a clustering center, and an early warning model, this method creates a transformer fault early warning model with self-learning ability and adaptive capabilities. After reasonable verification, the method becomes more universal and interpretable, and it can successfully conduct an early warning of transformer faults.

Suggested Citation

  • Xiaoqiang Liu & Ji Li & Lei Shao & Hongli Liu & Lei Ren & Lihua Zhu, 2023. "Transformer Fault Early Warning Analysis Based on Hierarchical Clustering Combined with Decision Trees," Energies, MDPI, vol. 16(3), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1168-:d:1042809
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1168/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1168/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Liangwei & Lin, Jing & Karim, Ramin, 2015. "An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 482-497.
    2. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    3. Xiaolu Zhang & Zeshui Xu, 2015. "Hesitant fuzzy agglomerative hierarchical clustering algorithms," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(3), pages 562-576, February.
    4. Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "Introduction to Data Mining," Springer Optimization and Its Applications, in: Data Mining in Agriculture, chapter 0, pages 1-21, Springer.
    Full references (including those not matched with items on IDEAS)

    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. Claudia Quinteros-Cartaya & Guillermo Solorio-Magaña & Francisco Javier Núñez-Cornú & Felipe de Jesús Escalona-Alcázar & Diana Núñez, 2023. "Microearthquakes in the Guadalajara Metropolitan Zone, Mexico: evidence from buried active faults in Tesistán Valley, Zapopan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 2797-2818, April.
    2. Katarzyna Hampel & Paulina Ucieklak-Jez & Agnieszka Bem, 2021. "Health System Responsiveness in the Light of the Euro Health Consumer Index," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 659-667.
    3. Kim, Junyung & Shah, Asad Ullah Amin & Kang, Hyun Gook, 2020. "Dynamic risk assessment with bayesian network and clustering analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    4. Roberts, Leigh, 2014. "Consistent estimation of breakpoints in time series, with application to wavelet analysis of Citigroup returns," Working Paper Series 18815, Victoria University of Wellington, School of Economics and Finance.
    5. David G Mets & Michael S Brainard, 2018. "An automated approach to the quantitation of vocalizations and vocal learning in the songbird," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-29, August.
    6. Michael Brusco & J Dennis Cradit & Douglas Steinley, 2021. "A comparison of 71 binary similarity coefficients: The effect of base rates," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-19, April.
    7. Noah E. Friedkin, 1984. "Structural Cohesion and Equivalence Explanations of Social Homogeneity," Sociological Methods & Research, , vol. 12(3), pages 235-261, February.
    8. Ota, Shuhei & Kimura, Mitsuhiro, 2017. "A statistical dependent failure detection method for n-component parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 376-382.
    9. David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler, 2011. "Measuring globalization: A hierarchical network approach," CREMA Working Paper Series 2011-11, Center for Research in Economics, Management and the Arts (CREMA).
    10. Balepur, Prashant Narayan, 1998. "Impacts of Computer-Mediated Communication on Travel and Communication Patterns: The Davis Community Network Study," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6cb1f85c, Institute of Transportation Studies, UC Berkeley.
    11. İsmail Güzel & Atabey Kaygun, 2022. "A new non-archimedean metric on persistent homology," Computational Statistics, Springer, vol. 37(4), pages 1963-1983, September.
    12. Lisa Price, 2001. "Demystifying farmers' entomological and pest management knowledge: A methodology for assessing the impacts on knowledge from IPM-FFS and NES interventions," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 18(2), pages 153-176, June.
    13. Elisa Frutos-Bernal & Ángel Martín del Rey & Irene Mariñas-Collado & María Teresa Santos-Martín, 2022. "An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    14. Geert Soete & Wayne DeSarbo & J. Carroll, 1985. "Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 173-192, December.
    15. Silvia Blasi & Edoardo Gobbo & Silvia Rita Sedita, 2022. "Big Data for smart cities and citizen engagement: evidence from Twitter data analysis on Italian municipalities," Working Papers - Business wp2022_01.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    16. Teh, Boon Kin & Goo, Yik Wen & Lian, Tong Wei & Ong, Wei Guang & Choi, Wen Ting & Damodaran, Mridula & Cheong, Siew Ann, 2015. "The Chinese Correction of February 2007: How financial hierarchies change in a market crash," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 225-241.
    17. Dalila B. M. M. Fontes & Seyed Mahdi Homayouni, 2019. "Joint production and transportation scheduling in flexible manufacturing systems," Journal of Global Optimization, Springer, vol. 74(4), pages 879-908, August.
    18. Yoshio Takane & Forrest Young & Jan Leeuw, 1977. "Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 42(1), pages 7-67, March.
    19. Fernández, D. & Arnold, R. & Pledger, S., 2016. "Mixture-based clustering for the ordered stereotype model," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 46-75.
    20. Wentao Qu & Xianchao Xiu & Huangyue Chen & Lingchen Kong, 2023. "A Survey on High-Dimensional Subspace Clustering," Mathematics, MDPI, vol. 11(2), pages 1-39, January.

    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:16:y:2023:i:3:p:1168-:d:1042809. 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.