Jensen-ANN: A Machine Learning adaptation of Jensen Wake Model

Wake models play an important role in wind farm layout optimization and control studies and it is, therefore, important to model wake effects in accurate and efficient ways. The power production from a wind farm is estimated using analytical models such as Jensen model in the wind industry, as they are simple and their computational cost is significantly less compared to the high-fidelity models involving Large Eddy Simulations. As these analytical models are used in an iterative setup like control and optimization of wind layouts, simulation cost involved in these models is extremely important. However, most of the analytical models assume linear expansion of wake while modeling wake effects, which makes it inaccurate. In this paper, the authors present a data driven approach under the framework of machine learning to impart the effect of nonlinear expansion of wake in the analytical models and thereby empowering them to be more accurate. Such a wake model was developed by integrating Artificial Neural Networks (ANNs) and Jensen model, where the expansion of wake is assumed as nonlinear and is modeled using ANNs inside the Jensen model setup, thus establishing the nonlinearity between inter-turbine distance and reduction in wind speed. To prove the efficacy of the proposed model, the results are compared with the predictions of analytical Jensen model, and it has been shown that the proposed model performs better than Jensen, demonstrating the importance of nonlinear expansion of wake and its effect in power calculation of a wind farm.