Probabilistic sensitivity analysis of offshore wind turbines using a transformed Kullback-Leibler divergence

Abstract

Characterizing uncertainty in complex systems is steadily growing as a topic of interest. One of the efficient ways to characterize a complex system is achieved by probabilistic sensitivity analysis. In the context of this type of analysis, there are a limited number of methods that quantify the change of the output to its full probabilistic extent. Moreover, in some engineering applications, such as reliability analysis, some established indicators of sensitivity do not fit the best interest of the analysis procedure.

This is the case of Kullback-Leibler divergence. Despite applied for probabilistic sensitivity analysis, it has limited interest in certain circumstances. A transformation of this indicator of entropy between two distributions is proposed in the present work. This transformation is used to establish a complementary indicator that is more perceptive, and more efficient for reliability sensitivity analysis. This new function is applied to research the global sensitivity analysis of an offshore wind turbine on a monopile foundation.

Results show that, for engineering problems as the one presented, the usage of this transformed indicator produces intuitive results. It allows the efficient identification of relevant states of operation as well as the most influent variables in the design of experiments, resulting in better comprehension of system’s behaviour and operational risks.