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Enhancing resilience analysis of power systems using robust estimation

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  • Shen, Lijuan
  • Tang, Loon Ching

Abstract

It has been well-recognized that the distribution of the blackout size of a power grid system has a heavy tail. The power-law distribution is a popular model for the heavy-tail phenomenon, and it is widely used in power system disruptions. However, there are significant reporting errors in the disruption data reported in public available databases, such as the database of the Electric Disturbance Events (OE-417) maintained by the US Department of Energy. Traditional inference techniques such as the maximum likelihood estimation can be sensitive to such contaminated data due to the reporting errors. In this paper, we propose a robust estimation procedure for the power-law distribution based on the minimum distance estimation method. A comprehensive simulation is used to evaluate the performance of the proposed method, and compare the performance with the existing maximum likelihood method. It is found that the proposed method outperforms the existing maximum likelihood method in the presence of contaminated data. We apply the proposed method to the blackout data from Jan 2002 to Aug 2016 based on the OE-417 database.

Suggested Citation

  • Shen, Lijuan & Tang, Loon Ching, 2019. "Enhancing resilience analysis of power systems using robust estimation," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 134-142.
    • Handle: RePEc:eee:reensy:v:186:y:2019:i:c:p:134-142
    DOI: 10.1016/j.ress.2019.02.022
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    References listed on IDEAS

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    Cited by:

    1. Sonal, & Ghosh, Debomita, 2022. "Impact of situational awareness attributes for resilience assessment of active distribution networks using hybrid dynamic Bayesian multi criteria decision-making approach," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    2. Zhang, Xi & Tu, Haicheng & Guo, Jianbo & Ma, Shicong & Li, Zhen & Xia, Yongxiang & Tse, Chi Kong, 2021. "Braess paradox and double-loop optimization method to enhance power grid resilience," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Fan, Dongming & Lin, Jing & Cai, Baoping & Liu, Bin, 2021. "Robustness of maintenance support service networks: attributes, evaluation and improvement," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    4. Wang, Shuliang & Guo, Zhaoyang & Huang, Xiaodi & Zhang, Jianhua, 2024. "A three-stage model of quantifying and analyzing power network resilience based on network theory," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Shen, Lijuan & Tang, Yanlin & Tang, Loon Ching, 2021. "Understanding key factors affecting power systems resilience," Reliability Engineering and System Safety, Elsevier, vol. 212(C).

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