Numerical optimisation of a small-scale wind turbine through the use of surrogate modelling

Abstract

Wind conditions in South Africa are suitable for small-scale wind turbines, with wind speeds below 7 m.s-1. This investigation is about a methodology to optimise a full wind turbine using a surrogate model. A previously optimised turbine was further optimised over a range of wind speeds in terms of a new parameterisation methodology for the aerodynamic profile of the turbine blades, using non-uniform rational B-splines to encompass a wide range of possible shapes. The optimisation process used a genetic algorithm to evaluate an input vector of 61 variables, which fully described the geometry, wind conditions and rotational speed of the turbine. The optimal performance was assessed according to a weighted coefficient of power, which rated the turbine blade's ability to extract power from the available wind stream. This methodology was validated using XFOIL to assess the final solution. The results showed that the surrogate model was successful in providing an optimised solution and, with further refinement, could increase the coefficient of power obtained.