Variable importance analysis of wind turbine extreme responses with Shapley value explanation

Abstract

As a leading form of renewable energy, wind power is gaining prominence with its decreasing levelized cost of electricity. The wind turbine extreme analysis is crucial for the structural reliability and integrity of wind turbines. Variable importance analysis provides a quantitative assessment of how significantly each variable influences model predictions. Variance-based importance measure quantifies each input variable’s contribution to the overall variance of a model or system. Such analyses are critical for interpreting the model and selecting features, thereby facilitating informed decision-making. This study uses Shapley value explanations to conduct a variable importance analysis of three wind turbine extreme responses, blade tip-tower clearance, blade root bending moment, and tower base bending moment. Random forest-based regression models and an improved Shapley effect evaluation algorithm are employed to improve the efficiency of the numerical analysis. The findings reveal the relative importance of five inputs, two wind parameters, one aerodynamic parameter, and two structural parameters, on each wind turbine extreme response variable. Additionally, the study explores how different dependency structures and varying correlation coefficients, even within the same correlation structure, affect the outcomes of variable importance analysis.