Surrogate modelling for ice accretion prediction on wind turbine blades based on support vector regression

Abstract

AbstractIce accretion poses a significant challenge, threatening the safety and operational stability of wind turbines in extreme cold weather. Accurate estimation of ice accretion is essential in minimizing this concern. Data‐driven techniques typically rely on extensive historical datasets, and simulation‐based methods may be time‐consuming. To address these challenges, surrogate modelling for ice accretion prediction is proposed, utilizing support vector regression. This surrogate model undergoes rigorous evaluation to ensure reliability. The surrogate model reduces time consumption by four orders of magnitude compared to simulation, with only a 2.5% decrease in accuracy. The analysis reveals that real‐time pitch angle adjustments, informed by both the surrogate model and meteorological data, can effectively reduce ice accretion on wind turbine blades in cold climates.