Optimum Design of Scaled-Down Wind Turbines Based on Field Data

Abstract This paper presents a method for designing wind turbine-scaled models based on field data. The development of more giant wind turbines significantly contributes to power generation with renewable sources. Thus, scaling models are a crucial element in the design process of large wind turbines. The scaling process requires careful consideration of the system’s physical laws and similarity criteria. Scaling methods depend on the prototype’s operating conditions (full-size wind turbine) and the experimental conditions (scaled model dimensions and wind tunnel capabilities). The data from a field turbine is the input for creating a scaled model and testing it in a wind tunnel. However, in general, there are few field data available. The task is not straightforward since most operating conditions must be satisfied, and the similarity criteria could result in different scaled models and wind tunnel conditions. The drawback of the scaling models is their inability to reproduce all the operating conditions. The method presented here considers most of the recommended similarities, including the dynamic modeling of the system. The final scaled-model design was obtained by modifying the blade profile to reproduce similar power, pitch momentum, and trust coefficients, keeping the same twist angle as the prototype. Since the possible profiles are too large, the best approximation was obtained by comparing the coefficient curves of different blade profiles with the prototype’s curves. The curves were calculated using the BEM method. It was found that the scaled model has an entirely different design.