A probabilistic framework based on statistical learning theory for structural reliability analysis of transmission line systems

Abstract

This paper describes a novel application of statistical learning theory to structural reliability analysis of transmission lines considering the uncertainties of climatic variables such as, wind speed, ice thickness and wind angle, and of the resistance of structural elements. The problem of reliability analysis of complex structural systems with implicit limit state functions is addressed by statistical model selection, where the goal is to select a surrogate model of the finite element solver that provides the value of the performance function for each conductor, insulator or tower element. After determining the performance function for each structural element, Monte Carlo simulation is used to calculate their failure probabilities. The failure probabilities of towers and the entire line are then estimated from the failure probabilities of their elements/components considering the correlation between failure events. In order to quantify the relative importance of line components and provide the engineers with a practical decision tool, the paper presents the calculation of two types of component importance measures. The presented methodology can be used to achieve optimised design, and to assess upgrading strategies to increase the line capacity.

Keywords:

• Structural reliability analysis
• implicit performance function
• transmission lines
• climatic hazards
• statistical learning theory
• Monte Carlo simulation

Acknowledgements

The authors gratefully acknowledge the support and generosity of Natural Sciences and Engineering Research Council of Canada (NSERC), The Research Chair Hydro-Québec/RTE on the structural and mechanical aspects of overhead transmission lines and TransÉnergie, without which the present study could not have been completed. The authors also truly appreciate the constructive comments and suggestions from Mr. Herve Ducloux.