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Availability importance measures of components in smart electric power grid systems

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  • Zheng, Junjun
  • Okamura, Hiroyuki
  • Pang, Taoming
  • Dohi, Tadashi

Abstract

The emergence of the smart grid has brought great innovation in the new distribution power system, facilitated a green and sustainable energy-based society, and mitigated the growing energy crisis. The smart grid is regarded as the next generation electrical power grid. It not only improves the power distribution systems with the techniques of distributed generation, but also makes the systems more complicated than the traditional ones. Thus it is important to evaluate the availability of smarts grids and to indicate the vulnerable parts of the systems. In this paper, a hierarchical model consisting of a layered fault tree (FT) and continuous-time Markov chains (CTMCs) is presented to model a smart grid system with two different power supply modes. We also analyze the component importance of the system, aiming to find the weak parts of the system thereby improving the system design. The importance analysis of components is based on parametric sensitivities and binary decision diagram (BDD) representation for the FTs. In a numerical illustration, we quantify the availability of the system with two power supply modes and also evaluate the importance of all components in the system.

Suggested Citation

  • Zheng, Junjun & Okamura, Hiroyuki & Pang, Taoming & Dohi, Tadashi, 2021. "Availability importance measures of components in smart electric power grid systems," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:reensy:v:205:y:2021:i:c:s0951832020306657
    DOI: 10.1016/j.ress.2020.107164
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    References listed on IDEAS

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    1. Guopeng Song & Hao Chen & Bo Guo, 2014. "A Layered Fault Tree Model for Reliability Evaluation of Smart Grids," Energies, MDPI, vol. 7(8), pages 1-23, July.
    2. Volkanovski, Andrija & ÄŒepin, Marko & Mavko, Borut, 2009. "Application of the fault tree analysis for assessment of power system reliability," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1116-1127.
    3. Abdul Rahman, Fariz & Varuttamaseni, Athi & Kintner-Meyer, Michael & Lee, John C., 2013. "Application of fault tree analysis for customer reliability assessment of a distribution power system," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 76-85.
    4. Markovic, Dragan S. & Zivkovic, Dejan & Branovic, Irina & Popovic, Ranko & Cvetkovic, Dragan, 2013. "Smart power grid and cloud computing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 566-577.
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    Cited by:

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    3. Yılmaz, Emre & German, Brian J. & Pritchett, Amy R., 2023. "Optimizing resource allocations to improve system reliability via the propagation of statistical moments through fault trees," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
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    6. 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).
    7. Fang, Xiaoyu & Qu, Jianfeng & Chai, Yi, 2023. "Self-supervised intermittent fault detection for analog circuits guided by prior knowledge," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    8. Wu, Chia-Huang & Yen, Tseng-Chang & Wang, Kuo-Hsiung, 2021. "Availability and Comparison of Four Retrial Systems with Imperfect Coverage and General Repair Times," Reliability Engineering and System Safety, Elsevier, vol. 212(C).

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