IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v320y2022ics0306261922005992.html
   My bibliography  Save this article

A multi-model-integration-based prediction methodology for the spatiotemporal distribution of vulnerabilities in integrated energy systems under the multi-type, imbalanced, and dependent input data scenarios

Author

Listed:
  • Sun, Chenhao
  • Zhou, Zhuoyu
  • Zeng, Xiangjun
  • Li, Zewen
  • Wang, Yuanyuan
  • Deng, Feng

Abstract

The reliability of an integrated energy system (IES) is most likely menaced by its weakest spots, and hence the prediction-based maintenance (PBM) is deployed to forecast these risky periods and locations where inspection and maintenance (I&M) actions are requisite. The key to accomplishing this is the pinpoint vulnerabilities prediction with sufficient lead time to prepare the PBM. With such motivations, one short-term prediction methodology for the spatiotemporal distribution of vulnerabilities, the fuzzy association pattern recognition covering rare and dependent factors (FAPRrdf), is developed in this paper. In the first preprocessing stage, a parallel learning process is formed. The discrete and continuous features are assessed through two expert models, respectively, to differentiate the common and rare components in each feature; Secondly, in the qualitative analysis, a two-expert-systems-integrated single entity approach is established to distinguish the “risky” components. The separated common and rare components are further evaluated to extract the high-impact (HI) and high-impact-low-probability (HILP) components, respectively. Ergo, the imbalanced data distribution can be solved, and the optimization step of weights between two independent expert models in some ensemble methods is no longer required; The third step is the quantitative analysis, and a structure importance measure (SIM)-based impact weight evaluation framework is proposed to rate the specific risk level of the “risky” components. The impact of each component on the variation of total system risks, as well as all the potential paths that will lead to a fault event in this system, are incorporated. Thereof, both the self-impacts of each component and the dependence on other components can be taken into account. Finally, this methodology is validated via an empirical case study, and its flexibility and feasibility during real applications can therefore be demonstrated.

Suggested Citation

  • Sun, Chenhao & Zhou, Zhuoyu & Zeng, Xiangjun & Li, Zewen & Wang, Yuanyuan & Deng, Feng, 2022. "A multi-model-integration-based prediction methodology for the spatiotemporal distribution of vulnerabilities in integrated energy systems under the multi-type, imbalanced, and dependent input data sc," Applied Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:appene:v:320:y:2022:i:c:s0306261922005992
    DOI: 10.1016/j.apenergy.2022.119239
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922005992
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119239?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. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.
    2. Hong, Jichao & Wang, Zhenpo & Yao, Yongtao, 2019. "Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Ilbahar, Esra & Kahraman, Cengiz & Cebi, Selcuk, 2021. "Location selection for waste-to-energy plants by using fuzzy linear programming," Energy, Elsevier, vol. 234(C).
    4. Sun, Chenhao & Wang, Xin & Zheng, Yihui, 2020. "An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks," Applied Energy, Elsevier, vol. 258(C).
    5. Sapountzoglou, Nikolaos & Lago, Jesus & De Schutter, Bart & Raison, Bertrand, 2020. "A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids," Applied Energy, Elsevier, vol. 276(C).
    6. Cadini, Francesco & Agliardi, Gian Luca & Zio, Enrico, 2017. "A modeling and simulation framework for the reliability/availability assessment of a power transmission grid subject to cascading failures under extreme weather conditions," Applied Energy, Elsevier, vol. 185(P1), pages 267-279.
    7. Wang, Haizheng & Zhao, Jian & Sun, Qian & Zhu, Honglu, 2019. "Probability modeling for PV array output interval and its application in fault diagnosis," Energy, Elsevier, vol. 189(C).
    8. Qu, Fuming & Liu, Jinhai & Zhu, Hongfei & Zhou, Bowen, 2020. "Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic," Applied Energy, Elsevier, vol. 262(C).
    9. Jufri, Fauzan Hanif & Widiputra, Victor & Jung, Jaesung, 2019. "State-of-the-art review on power grid resilience to extreme weather events: Definitions, frameworks, quantitative assessment methodologies, and enhancement strategies," Applied Energy, Elsevier, vol. 239(C), pages 1049-1065.
    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. Sun, Chenhao & Xu, Hao & Zeng, Xiangjun & Wang, Wen & Jiang, Fei & Yang, Xin, 2023. "A vulnerability spatiotemporal distribution prognosis framework for integrated energy systems within intricate data scenes according to importance-fuzzy high-utility pattern identification," Applied Energy, Elsevier, vol. 344(C).
    2. Sun, Chenhao & Wang, Xin & Zheng, Yihui, 2020. "An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks," Applied Energy, Elsevier, vol. 258(C).
    3. Zeng, Zhiguo & Fang, Yi-Ping & Zhai, Qingqing & Du, Shijia, 2021. "A Markov reward process-based framework for resilience analysis of multistate energy systems under the threat of extreme events," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    4. Ahmadi, Somayeh & Saboohi, Yadollah & Vakili, Ali, 2021. "Frameworks, quantitative indicators, characters, and modeling approaches to analysis of energy system resilience: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    5. Liu, Hanchen & Wang, Chong & Ju, Ping & Li, Hongyu, 2022. "A sequentially preventive model enhancing power system resilience against extreme-weather-triggered failures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    6. Banghua Xie & Changfan Li & Zili Wu & Weiming Chen, 2021. "Topological Modeling Research on the Functional Vulnerability of Power Grid under Extreme Weather," Energies, MDPI, vol. 14(16), pages 1-27, August.
    7. Sang, Maosheng & Ding, Yi & Bao, Minglei & Li, Siying & Ye, Chengjin & Fang, Youtong, 2021. "Resilience-based restoration strategy optimization for interdependent gas and power networks," Applied Energy, Elsevier, vol. 302(C).
    8. Mortensen, Lasse Kappel & Shaker, Hamid Reza & Veje, Christian T., 2022. "Relative fault vulnerability prediction for energy distribution networks," Applied Energy, Elsevier, vol. 322(C).
    9. Adriana Mar & Pedro Pereira & João F. Martins, 2019. "A Survey on Power Grid Faults and Their Origins: A Contribution to Improving Power Grid Resilience," Energies, MDPI, vol. 12(24), pages 1-21, December.
    10. Yu, Min Gyung & Pavlak, Gregory S., 2023. "Risk-aware sizing and transactive control of building portfolios with thermal energy storage," Applied Energy, Elsevier, vol. 332(C).
    11. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    12. Zhu, Jianhua & Peng, Yan & Gong, Zhuping & Sun, Yanming & Lai, Chaoan & Wang, Qing & Zhu, Xiaojun & Gan, Zhongxue, 2019. "Dynamic analysis of SNG and PNG supply: The stability and robustness view #," Energy, Elsevier, vol. 185(C), pages 717-729.
    13. Yao He & Yongchun Yang & Meimei Wang & Xudong Zhang, 2022. "Resilience Analysis of Container Port Shipping Network Structure: The Case of China," Sustainability, MDPI, vol. 14(15), pages 1-17, August.
    14. Fauzan Hanif Jufri & Jun-Sung Kim & Jaesung Jung, 2017. "Analysis of Determinants of the Impact and the Grid Capability to Evaluate and Improve Grid Resilience from Extreme Weather Event," Energies, MDPI, vol. 10(11), pages 1-17, November.
    15. Yang, Jian & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Zhao, Qinghai & Meng, Zewen, 2021. "Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 233(C).
    16. Sang-Guk Yum & Kiyoung Son & Seunghyun Son & Ji-Myong Kim, 2020. "Identifying Risk Indicators for Natural Hazard-Related Power Outages as a Component of Risk Assessment: An Analysis Using Power Outage Data from Hurricane Irma," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    17. Zhijie Duan & Luo Zhang & Lili Feng & Shuguang Yu & Zengyou Jiang & Xiaoming Xu & Jichao Hong, 2021. "Research on Economic and Operating Characteristics of Hydrogen Fuel Cell Cars Based on Real Vehicle Tests," Energies, MDPI, vol. 14(23), pages 1-19, November.
    18. Popović, Željko N. & KovaÄ ki, Neven V. & Popović, Dragan S., 2020. "Resilient distribution network planning under the severe windstorms using a risk-based approach," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    19. Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
    20. Chen, Lei & Jiang, Yuqi & Zheng, Shencong & Deng, Xinyi & Chen, Hongkun & Islam, Md. Rabiul, 2023. "A two-layer optimal configuration approach of energy storage systems for resilience enhancement of active distribution networks," Applied Energy, Elsevier, vol. 350(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:appene:v:320:y:2022:i:c:s0306261922005992. 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: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.