Investigation of Wake Redirection Techniques for Wind Farms

Abstract

A world-wide increase in wind energy has seen an increase in the need for optimisation of wind farms. In a wind farm, an upstream turbine has several negative effects on the downstream turbine. The upstream turbine extracts a lot of the energy from the freestream, leaving little energy remaining in the wake to be extracted by the downstream turbine. There is also a significantly higher turbulence intensity in the wake leading to uneven loads being exerted. This all results in reduced power outputs of the wind farm as a whole. Wake redirection techniques, and specifically yaw misalignment, could reduce these negative effects and improve the overall power output of a wind farm. The first aim of this study is to investigate the ability of a surrogate model to predict the flow field when the upstream turbine is at different yaw angles. This could assist with real-time wake analysis and optimisation of a wind farm, without the need for running high-fidelity simulations to predict the wake when the yaw angle of the turbine is changed. The second aim is to investigate the physical effect of the upstream turbine yaw on the wake, as well as the interaction between the wake and the downstream turbine. A surrogate model is a combination of a Reduced Order Model (ROM) and a Machine Learning (ML) algorithm. A ROM was created using an existing, open-source dataset that conducted Large Eddy Simulations (LES) on a two-turbine array using the Simulator for Wind Farm Applications (SOWFA) software. The Dynamic Mode Decomposition (DMD) modes generated during this process were grouped according to the physical property of the wake that they represent. These modes were used to train the ML model to predict the development of individual modes at different yaw angles, which were then reconstructed in groups or combinations of groups to predict the entire flow field at the different yaw angles. It was found that the mode group related to the mean flow is essential to reconstruction and it alone results in the most accurate reconstructions with an average error of 7% and a reduction in computational resources by a factor of approximately 300 compared to LES. Smoke visualisation, hot-wire measurements and force tests were conducted to further investigate the physical properties of the wake. A difference of 8.1% was recorded between the experimentally measured maximum angle of deflection and the maximum generated from the surrogate model. It was found that the larger the yaw angle of the upstream turbine, the greater the wake deflection. This relationship is hypothesised to be linear until the tested yaw angle of 45°, however it is expected to plateau after it reaches its stall condition. As yaw increases, both the force in the horizontal direction (Fy) and the side toppling moment (Mx) decrease linearly with the yaw angle, suggesting that a larger yaw angle also helps reduce the structural loads on the downstream turbine.