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Reliability Measurement for Mixed Mode Failures of 33/11 Kilovolt Electric Power Distribution Stations

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  • Faris M Alwan
  • Adam Baharum
  • Geehan S Hassan

Abstract

The reliability of the electrical distribution system is a contemporary research field due to diverse applications of electricity in everyday life and diverse industries. However a few research papers exist in literature. This paper proposes a methodology for assessing the reliability of 33/11 Kilovolt high-power stations based on average time between failures. The objective of this paper is to find the optimal fit for the failure data via time between failures. We determine the parameter estimation for all components of the station. We also estimate the reliability value of each component and the reliability value of the system as a whole. The best fitting distribution for the time between failures is a three parameter Dagum distribution with a scale parameter and shape parameters and . Our analysis reveals that the reliability value decreased by 38.2% in each 30 days. We believe that the current paper is the first to address this issue and its analysis. Thus, the results obtained in this research reflect its originality. We also suggest the practicality of using these results for power systems for both the maintenance of power systems models and preventive maintenance models.

Suggested Citation

  • Faris M Alwan & Adam Baharum & Geehan S Hassan, 2013. "Reliability Measurement for Mixed Mode Failures of 33/11 Kilovolt Electric Power Distribution Stations," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    • Handle: RePEc:plo:pone00:0069716
    DOI: 10.1371/journal.pone.0069716
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    References listed on IDEAS

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    1. Filippo Domma & Sabrina Giordano & Mariangela Zenga, 2011. "Maximum likelihood estimation in Dagum distribution with censored samples," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(12), pages 2971-2985, March.
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    Cited by:

    1. Chunhui Guo & Chuan Lyu & Jiayu Chen & Dong Zhou, 2018. "A multi-event combination maintenance model based on event correlation," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-24, November.

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