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Reliability analysis with an icing model for estimating extreme events

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Abstract

The objective of the work presented here is to improve estimates of atmospheric icing hazards, specifically for equivalent radial ice accumulation (Req) on electrical transmission lines, by solving CRREL empirical icing model as a function of random variables using reliability methods. The propagation of uncertainty in the model is preformed using first-order reliability methods (FORM) and Monte Carlo simulations. This methodology is used on clustered freezing rainstorms that form the basis of a de-aggregate hazard analysis. In this paper, freezing rain storms were clustered based on anomaly maps constructed using NCEP reanalysis data of 1000–500 hPa geopotential heights or SLP. The procedure is demonstrated with data from Montreal. The physical meanings of the different clusters were also presented in terms of wind speed, total precipitation, air mass positions, and compare with Rauber’s archetypical patterns. For single population results, the design point identified by FORM analysis for high values of Req corresponds to high total precipitation, high freezing ratios, but only slightly higher than average wind speed. For the de-aggregated analysis, different design points are associated with each clusters. These results correspond to the measured physical characteristics of extreme storms associated with the clusters. In particular, the design point associated with the cluster containing the 1961 ice storm has relatively higher equivalent wind speeds then the other clusters. The performance function being nonlinear, the results from FORM are approximate making Monte Carlo simulations more appropriate for calculating return periods. The hazard function for Req derived from reliability methods for Montreal produces results similar to those of Jones and White in The estimation and application of extremes, electrical. Transmission in a New Age, ASCE, pp 32–47, (2002) using a superstation and extreme value analysis. For the 50-year return period, de-aggregate and single population reliability analysis gives similar results. The analysis indicates Req of approximately 35, 45 and 55 mm for return periods of 50, 100 and 200 years, respectively.

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Authors and Affiliations

  1. Stats-Tec, Montreal, QC, Canada

    Reza Erfani

  2. McGill University, Montreal, QC, Canada

    Luc Chouinard

  3. University of Sherbrooke, Sherbrooke, QC, Canada

    Frédéric Légeron

Authors
  1. Reza Erfani
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  2. Luc Chouinard
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  3. Frédéric Légeron
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Correspondence to Reza Erfani.

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Erfani, R., Chouinard, L. & Légeron, F. Reliability analysis with an icing model for estimating extreme events. Nat Hazards 82, 415–439 (2016). https://doi.org/10.1007/s11069-016-2191-6

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  • DOI: https://doi.org/10.1007/s11069-016-2191-6

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