Implementation of Deep Generative Model for Generating Synthetic Wind Speed Data for Offshore Wind Turbine Maintenance Exploration

The optimal maintenance strategy for wind turbines relies heavily on estimation by exploring wind speed scenarios, considering the dynamic nature of environmental conditions. In this study, we implement a deep generative model leveraging the Generative Adversarial Network (GAN) algorithm, emphasizing its application for generating synthetic time-series data, such as wind speed, known as DoppelGANger architecture. By generating synthetic wind speed scenarios that incorporate the complexity and variability of real-world wind patterns, the synthetic data provide a better understanding of how maintenance strategies can be tailored to different operational scenarios.