Keywords
power supply reliability
distribution electric networks
retrospective data
statistical modelling
Kolmogorov–Smirnov test
Pareto distribution
lognormal distribution
prioritization of reliability measures
How to Cite
Abstract
The paper proposes a method for decomposing the SAIDI reliability index based on the structure of outage durations using retrospective data on technological disturbances in electric power distribution networks. The method is grounded on a statistical analysis of power supply restoration times with identification of two characteristic outage regimes: short outages, primarily associated with fault location and switching operations, and long outages, additionally involving repair and restoration activities.
The boundary between the two regimes is determined formally by maximizing the agreement between the empirical distribution and the normal distribution according to the Kolmogorov–Smirnov criterion. It is shown that short outages are adequately approximated by a normal distribution, whereas long outages are best described by heavy‑tailed distributions, namely the lognormal or Pareto distributions.
Based on the proposed decomposition, the quantitative contribution of each regime to SAIDI formation is evaluated. The results demonstrate that long outages, despite their lower occurrence probability, account for a dominant share of the total interruption duration and therefore have a disproportionate impact on the SAIDI value.
The proposed method enables justification of reliability improvement priorities without transitioning to optimization problems, focusing instead on measures aimed at fault localization and reduction of repair and restoration times. The obtained results may be used as an engineering decision‑support tool in the development of reliability improvement programs under conditions of limited availability of detailed operational data. Ref. 14, fig. 4, table.
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