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Failure probability analysis of heliostat systems

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  • Samir Benammar
  • Kong Fah Tee

Abstract

Heliostats represent the most important maintenance cost in the solar power tower plant. The aim of this work is to provide a failure probability analysis for heliostat design in order to minimize this maintenance cost. Based on mechanics of material study and wind aerodynamic analysis, a performance function, with five random variables, has been developed wherein the random variables are: wind speed, inside and outside pedestal diameters, pedestal yield stress and mirror mass. Four main methods have been proposed: first order reliability method, second order reliability method, Monte Carlo (MC) method and subset simulation (SS) method. The variation of failure probability with the variation of pedestal wall thickness and wind speed, for different outside diameters and heliostat azimuth and elevation angles, has been simulated. The results show that SS is more efficient and accurate for small failure probabilities; however, MC is more accurate for high failure probabilities.

Suggested Citation

  • Samir Benammar & Kong Fah Tee, 2020. "Failure probability analysis of heliostat systems," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 16(4), pages 342-366.
    • Handle: RePEc:ids:ijcist:v:16:y:2020:i:4:p:342-366
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    Cited by:

    1. Samir Benammar & Kong Fah Tee, 2021. "Criticality Analysis and Maintenance of Solar Tower Power Plants by Integrating the Artificial Intelligence Approach," Energies, MDPI, vol. 14(18), pages 1-27, September.

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