IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v232y2023ics0951832022006585.html
   My bibliography  Save this article

A restoration-clustering-decomposition learning framework for aging-related failure rate estimation of distribution transformers

Author

Listed:
  • Huang, Wei
  • Shao, Changzheng
  • Hu, Bo
  • Li, Weizhan
  • Sun, Yue
  • Xie, Kaigui
  • Zio, Enrico
  • Li, Wenyuan

Abstract

Distribution transformers (DTs) are critical components used in power distribution networks, and they are vulnerable to aging failures due to irreversible insulation degradation. Therefore, the accurate estimation of the aging-related failure rates (AFRs) is necessary for the reliability-centered maintenance and replacement strategies needed for ensuring service reliability and safety. Various data-intensive models have been proposed for AFR evaluation of power equipment. However, these models cannot be used for AFR evaluation of DTs due to the limitation of the available data. This paper tackles this important problem in an unconventional way by it develops a novel Restoration-Clustering-Decomposition learning framework to model the AFRs of individual DTs and improve evaluation accuracy. The proposed approach requires only the non-intrusive data that can be directly extracted from existing available databases, making it feasible to be applying to numerous DTs. First, the analysis of the degree of polymerization (DP) degradation and the Latin Hypercube sampling (LHS) technique are combined to reproduce aging failure data. Then, an optimal Entropy-weighted K-means (EW-K-means) clustering method and the classic 2-parameter Weibull model are used to evaluate the average AFRs of different DT groups through failure data analysis. Then, a DP-based decomposition function is introduced to quantify the relative aging degree of in-group individuals and to derive the probabilistic AFRs of each DT in the group. Application examples of a scrapped DT population in Chongqing Electric Power Company of China are presented and discussed in detail. The results show that the proposed learning framework has a promising capability for AFR evaluation of individual DTs and bears great practicality in the real world.

Suggested Citation

  • Huang, Wei & Shao, Changzheng & Hu, Bo & Li, Weizhan & Sun, Yue & Xie, Kaigui & Zio, Enrico & Li, Wenyuan, 2023. "A restoration-clustering-decomposition learning framework for aging-related failure rate estimation of distribution transformers," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:reensy:v:232:y:2023:i:c:s0951832022006585
    DOI: 10.1016/j.ress.2022.109043
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022006585
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.109043?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Petchrompo, Sanyapong & Parlikad, Ajith Kumar, 2019. "A review of asset management literature on multi-asset systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 181-201.
    3. Dashti, Reza & Yousefi, Shaghayegh, 2013. "Reliability based asset assessment in electrical distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 129-136.
    4. Starling, James K. & Mastrangelo, Christina & Choe, Youngjun, 2021. "Improving Weibull distribution estimation for generalized Type I censored data using modified SMOTE," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    5. Gu, Bo & Zhang, Tianren & Meng, Hang & Zhang, Jinhua, 2021. "Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation," Renewable Energy, Elsevier, vol. 164(C), pages 687-708.
    6. Cai, Wei & Zhao, Jingyi & Zhu, Ming, 2020. "A real time methodology of cluster-system theory-based reliability estimation using k-means clustering," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    7. Liu, Bin & Liang, Zhenglin & Parlikad, Ajith Kumar & Xie, Min & Kuo, Way, 2017. "Condition-based maintenance for systems with aging and cumulative damage based on proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 200-209.
    8. Singh, Prashant & Pasha, Junayed & Moses, Ren & Sobanjo, John & Ozguven, Eren E. & Dulebenets, Maxim A., 2022. "Development of exact and heuristic optimization methods for safety improvement projects at level crossings under conflicting objectives," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    9. Ariannik, Mohamadreza & Razi-Kazemi, Ali A. & Lehtonen, Matti, 2020. "An approach on lifetime estimation of distribution transformers based on degree of polymerization," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    10. Liu, Cuiwei & Wang, Yazhen & Li, Xinhong & Li, Yuxing & Khan, Faisal & Cai, Baoping, 2021. "Quantitative assessment of leakage orifices within gas pipelines using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    11. Zhu, Tiefeng, 2020. "Reliability estimation for two-parameter Weibull distribution under block censoring," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    12. Dias, Luis & Leitão, Armando & Guimarães, Luis, 2021. "Resource definition and allocation for a multi-asset portfolio with heterogeneous degradation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    13. Zhou, Hang & Lopes Genez, Thiago Augusto & Brintrup, Alexandra & Parlikad, Ajith Kumar, 2022. "A hybrid-learning decomposition algorithm for competing risk identification within fleets of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    14. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
    15. Lu, Qin & Zhang, Wei, 2022. "Integrating dynamic Bayesian network and physics-based modeling for risk analysis of a time-dependent power distribution system during hurricanes," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    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. Zhang, Dingmao & Li, Gengfeng & Bie, Zhaohong & Fan, Kangjian, 2024. "An analytical method for reliability evaluation of power distribution system with time-varying failure rates," Reliability Engineering and System Safety, Elsevier, vol. 250(C).

    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. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Ariannik, Mohamadreza & Razi-Kazemi, Ali A. & Lehtonen, Matti, 2020. "An approach on lifetime estimation of distribution transformers based on degree of polymerization," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    3. Jiang, Renyan & Qi, Faqun & Cao, Yu, 2023. "Relation between aging intensity function and WPP plot and its application in reliability modelling," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Lima, Eliana Sangreman & Costa, Ana Paula Cabral Seixas, 2019. "Improving Asset Management under a regulatory view," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    5. Li, Bokang & Afkhami, Payam & Khayamim, Razieh & Elmi, Zeinab & Moses, Ren & Sobanjo, John & Ozguven, Eren E. & Dulebenets, Maxim A., 2024. "A holistic optimization-based approach for sustainable selection of level crossings for closure with safety, economic, and environmental considerations," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    6. Gjorgiev, Blazhe & Das, Laya & Merkel, Seline & Rohrer, Martina & Auger, Etienne & Sansavini, Giovanni, 2023. "Simulation-driven deep learning for locating faulty insulators in a power line," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    7. Dias, Luis & Leitão, Armando & Guimarães, Luis, 2021. "Resource definition and allocation for a multi-asset portfolio with heterogeneous degradation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    8. Chen, Zhen & Zhou, Di & Zio, Enrico & Xia, Tangbin & Pan, Ershun, 2023. "Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    9. Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    10. Ma, Zhonghai & Liao, Haitao & Gao, Jianhang & Nie, Songlin & Geng, Yugang, 2023. "Physics-Informed Machine Learning for Degradation Modeling of an Electro-Hydrostatic Actuator System," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    11. Zhou, Hang & Lopes Genez, Thiago Augusto & Brintrup, Alexandra & Parlikad, Ajith Kumar, 2022. "A hybrid-learning decomposition algorithm for competing risk identification within fleets of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    12. Sun, Quan & Peng, Fei & Yu, Xianghai & Li, Hongsheng, 2023. "Data augmentation strategy for power inverter fault diagnosis based on wasserstein distance and auxiliary classification generative adversarial network," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    13. Kristjanpoller, Fredy & Cárdenas-Pantoja, Nicolás & Viveros, Pablo & Pascual, Rodrigo, 2023. "Wind farm life cycle cost modelling based on oversizing capacity under load sharing configuration," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    14. Li, Yang & Gao, Haifeng & Chen, Hongtian & Liu, Chun & Yang, Zhe & Zio, Enrico, 2024. "Accelerated degradation testing for lifetime analysis considering random effects and the influence of stress and measurement errors," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    15. Zhou, Hang & Farsi, Maryam & Harrison, Andrew & Parlikad, Ajith Kumar & Brintrup, Alexandra, 2023. "Civil aircraft engine operation life resilient monitoring via usage trajectory mapping on the reliability contour," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    16. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    17. Mingfei Li & Jiajian Wu & Zhengpeng Chen & Jiangbo Dong & Zhiping Peng & Kai Xiong & Mumin Rao & Chuangting Chen & Xi Li, 2022. "Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning," Energies, MDPI, vol. 15(17), pages 1-20, August.
    18. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    19. Wang, Tianzhe & Chen, Zequan & Li, Guofa & He, Jialong & Liu, Chao & Du, Xuejiao, 2024. "A novel method for high-dimensional reliability analysis based on activity score and adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    20. Ajagekar, Akshay & You, Fengqi, 2021. "Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems," Applied Energy, Elsevier, vol. 303(C).

    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:eee:reensy:v:232:y:2023:i:c:s0951832022006585. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.