Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data

Wake interactions between wind turbines have a great impact on the overall performance of a wind farm. In this work, a novel deep learning method, called Bilateral Convolutional Neural Network (BiCNN), is proposed and then employed to accurately model dynamic wind farm wakes based on flow field data generated by high-fidelity simulations. Different from the existing machine-learning-based dynamic wake models where dimensionality reduction is essential, the proposed BiCNN is designed to directly process the different types of inputs through a background path and a foreground path, thus avoiding the errors due to dimensionality reduction. Substantial results show that the developed machine learning based wake model can achieve accurate wake predictions in real time, i.e. it captures the spatial variations of the dynamic wakes similarly as high-fidelity wake models and runs as fast as low-fidelity static wake models. The overall prediction error of the developed model is 3.7% with respect to the freestream wind speed. Furthermore, the results for a test farm consisting of 25 turbines show that the developed model can predict the dynamic wind farm wakes within several seconds using a standard laptop, while the same scenario using high-fidelity numerical models would consume tens of thousands of CPU hours.