The performance of an open-loop wake steering controller is investigated with a new wind tunnel experiment. Three scaled wind turbines are placed on a large turntable and exposed to a turbulent inflow, resulting in dynamically varying wake interactions. The study highlights the importance of using a robust formulation and plant flow models of appropriate fidelity, and the existence of possible margins for improvement by the use of dynamic controllers.

The article describes results of experimental wind tunnel and CFD testing of four different straight bladed vertical axis wind turbine model configurations. The experiment tested a novel concept of vertically dividing and azimuthally shifting a turbine rotor into two parts with a specific uneven height division in order to limit cycle amplitudes and average cycle values of bending moments at the bottom of the turbine shaft to increase product lifetime, especially for industrial scale turbines.

The potential of collective offshore wind power is quantified using 16 years of hourly wind speed observations. Wind power intermittency is reduced through a hypothetical electricity grid connecting five sites at the Norwegian continental shelf. We identify large-scale atmospheric situations resulting in long term periods of power shut-down. Wind power variability and risk-measures decrease in an interconnected wind power system.

Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting, and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, an artificial neural network (ANN), in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as ANN inputs in an effort to discern the most useful atmospheric information for this purpose.