Abstracted/indexed

This project is a comparative study that takes into consideration various airfoils from the Selig, NACA, and Eppler families and models them as diffusers of the wind turbine. The efficiency of the diffuser-augmented wind turbine can be enhanced by optimizing the geometry of the diffuser shape. Their subsequent performance trends were then analyzed, and the lower-performing airfoils were systematically eliminated to leave us with an optimum design.

This paper assesses the behaviour and causality of sudden variations in wind power generation over a short period of time, also called "ramp events". It is shown, amongst other things, that ramps at the study site are mostly associated with frontal activity. Overall, the research contributes to a better understanding of the drivers and behaviours of wind power ramps at the wind farm scale, beneficial to ramp forecasting and ramp modelling.

Currently, the available power estimation is highly dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a model-free approach for a single-input dynamic estimation of the available power using RNNs. The unsteady patterns are represented by LSTM neurons, and the network is adapted to changing inflow conditions via transfer learning. Including highly turbulent flows, the validation shows easy compliance with the grid codes.

With the increase in installed wind capacity, the rotor diameter of wind turbines is becoming larger and larger, and therefore it is necessary to take aeroelasticity into consideration. At the same time, wind turbines are in reality subjected to atmospheric inflow leading to high wind instabilities and fluctuations. Within this work, a high-fidelity chain is used to analyze the effects of both by the use of models of the same turbine with increasing complexity and technical details.