A Generative Adversarial Networks based Modelling for Efficient Design of Wind Energy Conversion Systems

Climate change and global energy crisis are two primary drivers behind the search of several forms of renewable energy sources e.g., wind energy. In studies of wind farm layout design and control, wind frequency map (WFM) plays a crucial role. WFM is a joint probability mapping between wind direction and speed considering their time series behavior. The limited past data suppresses the ability to represent long term variabilities in wind which leads to inaccurate estimation of WFMs resulting in unrealistic calculation of wind power for wind farms. Hence, in this paper, Generative Adversarial Networks (GANs) are explored for generation of wind frequency maps using limited wind time series data. GAN s are data driven probabilistic techniques, which captures distribution hidden in data. The distribution of wind scenarios, once captured by GANs, can generate new scenarios. The success of GAN's ability in capturing the probability distribution hidden in a wind dataset was demonstrated by calculating power production from an optimal layout using the available scenarios and scenarios generated by GANs. Trained GANs can generate many scenarios in future horizon which can be used for robust design of wind farms. Thus, this study can be helpful in efficient design and control of wind farms under wind state uncertainty.