50-year return-period wet-snow load estimation based on weather station data for overhead line design in France

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Abstract

Historically, as far as wet-snow loads were concerned, overhead line design was often based on experience or long-term applications with positive results. New standard like CENELEC EN 50341-1 (2012) take into account characteristic loads, i.e. 50-year return-period loads, for the overhead line design. This article proposes a method to estimate characteristic wet-snow loads based on meteorological data recorded at weather stations. The model used to calculate those loads is mainly inspired by a recent article written by Nygaard et al. (2013a) in which a new parameterization is proposed for the classical cylindrical wet-snow accretion model, as described in ISO 12494 standard (2001), annex C. After a complete description of the model and its parameterization adapted to French wet-snow events, the statistical issues are examined. Then, the model is used with the meteorological data of 87 weather stations in order to calculate wet-snow loads whose relevance has been positively tested according to real damages recorded in a complete wet-snow event database. At last, the characteristic loads of those 87 stations have been determined according to all the loads generated by the model and processed by a peak-over-threshold (POT) method. A practical method to determine the 90% confidence intervals of those characteristic values is given. As it is demonstrated that there is only one value of the ice class (IC) masses proposed by ISO 12494 in each confidence interval, characteristic loads can be easily expressed in terms of ICs. That method shows that ICs ranging from R1 (0.5 kg mg-1) to R5 (5 kg mg-1) could be used for overhead line design in France.

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Ducloux, H., & Nygaard, B. E. (2014). 50-year return-period wet-snow load estimation based on weather station data for overhead line design in France. Natural Hazards and Earth System Sciences, 14(11), 3031–3041. https://doi.org/10.5194/nhess-14-3031-2014

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