Enhancing Wind Turbine Dynamic Transient Simulation Using Deep Learning

The simulation of wind turbines under dynamic conditions, such as voltage dips, is a critical aspect in ensuring the stability of power systems with high levels of wind energy integration. IEC 61400-27 standard provides a common framework for the dynamic simulation of wind turbines through the use of generic models. However, the calibration of these models’ parameters is an essential task to ensure that the simulation models behave in the same way as real wind turbines. This work presents the results obtained by utilizing two different deep learning frameworks, PyTorch and Flax, to demonstrate how the combination of traditional simulation tools and deep learning techniques can enhance the simulation of wind turbine models under transient conditions. The proposed approach identifies some of the parameters that most effectively capture the turbine’s behaviour, enabling precise simulations across diverse operational scenarios.