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A vulnerability spatiotemporal distribution prognosis framework for integrated energy systems within intricate data scenes according to importance-fuzzy high-utility pattern identification

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  • Sun, Chenhao
  • Xu, Hao
  • Zeng, Xiangjun
  • Wang, Wen
  • Jiang, Fei
  • Yang, Xin

Abstract

One key task for the planning and organizing of Inspection and Maintenance (I&M) works in an integrated energy system (IES) is to handle its weakest vulnerabilities, that is, when and where in this system the I&M actions or countermeasures ought to be performed. This requires a pinpoint spatiotemporal distribution prediction of these vulnerabilities, then more preparation times can be brought about for utilities to appropriately allocate the limited I&M manpower and equipment. With such a motivation, in light of both the internal and surrounding conditions, this paper proposes a prognosis framework, namely the Importance-Fuzzy High-utility Pattern Identification with lifetime-dependent factors (IFHPIlf). The IFHPIlf aims for the component-vulnerability patterns with higher profits rather than simply higher frequencies. In that case, when applied in imbalanced distributed databases, this framework can incorporate the High-Impact-Low-Probability (HILP) components as well, without the extra steps of separating and assessing rare and common components. For the purpose of assessing the utilities of each component and transaction, a parallel learning architecture is formulated to evaluate the corresponding two attributes, unit price and quantity, respectively. To estimate the unit price, the risk level of each component will be quantitatively rated via the built BP Time-dependent-lifetime Importance (BPTdlI) model, where the direct impacts of each component’s lifetime distribution on the reliability distribution of the whole system, as well as the entire underlying system failure-related hazard cut sets, are integrated. Ergo, the data dependence in temporal scales can be taken into account; To qualitatively differentiate the perilous components, the quantity will be calculated in the established Importance-Fuzzy High-utility Pattern Identification (IFHPI) model, wherein the time-dependent-lifetime important FIS are conjugated with the Fuzzy Interest-Utility Measures (FIUM). Hence, both the discrete and continuous features will be handled in the same entity, and the determinations can be straightforwardly based on their influences. At last, the flexibility and feasibility of this framework during applications are demonstrated in terms of an empirical case study.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:344:y:2023:i:c:s030626192300586x
    DOI: 10.1016/j.apenergy.2023.121222
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