Analyzing the Temporal Variation of Wind Turbine Responses Using Gaussian Mixture Model and Gaussian Discriminant Analysis

Abstract

Site-and time-specific wind field characteristics have a significant impact on the structural response and the lifespan of wind turbines. This paper presents a machine learning approach towards analyzing and predicting the response of a wind turbine structure to diurnal and nocturnal wind fields. Machine learning algorithms are applied (i) to better understand the changes of wind field characteristics due to atmospheric conditions, and (ii) to gain insights into the wind turbine loads being affected by the wind field. Using Gaussian Mixture Model, the variations in wind field characteristics are 2 investigated by comparing the joint probability density functions of selected wind field features. The wind field features are constructed from long-term monitoring data taken from a 500 kW wind turbine in Germany that is used as a reference system. Furthermore, employing Gaussian Discriminant Analysis, representative daytime and nocturnal wind turbine loads are compared and analyzed.