Nonparametric importance sampling for wind turbine reliability analysis with stochastic computer models

Abstract

Using aeroelastic stochastic simulations, this study presents an importance sampling method for assessing wind turbine reliability. As the size of modern wind turbines gets larger, structural reliability analysis becomes more important to prevent any catastrophic failures. At the design stage, operational data do not exist or are scarce. Therefore, aeroelastic simulation is often employed for reliability analysis. Importance sampling is one of the powerful variance reduction techniques to mitigate computational burden in stochastic simulations. In the literature, wind turbine reliability assessment with importance sampling has been studied with a single variable, wind speed. However, other atmospheric stability conditions also impose substantial stress on the turbine structure. Moreover, each environmental factor's effect on the turbine's load response depends on other factors. This study investigates how multiple environmental factors collectively affect the turbine reliability. Specifically, we devise a new nonparametric importance sampling method that can quantify the contributions of each environmental factor and its interactions with other factors, while avoiding computational problems and data sparsity issue arising in rare event simulation. Our wind turbine case study and numerical examples demonstrate the advantage of the proposed approach.