Adaptive neuro-fuzzy generalization of wind turbine wake added turbulence models

When the turbine extracts power from the wind, a wake evolves downstream of the turbine. The turbines operating in the wake are not only subjected to a decreased wind speed but also increased dynamic loading arising from the increased turbulence induced by the upstream turbines. This increased turbulence must be accounted, when selecting a turbine. This increase in turbulence intensity can imply a significant reduction in the fatigue lifetime of wind turbines placed in wakes. For this reason, a large number of studies have been established concerning the calculation of wake added turbulence. Even though a number of mathematical functions have been proposed for modeling the wake added turbulence, there are still disadvantages of the models like very demanding in terms of calculation time. Artificial neural networks (ANN) can be used as alternative to analytical approach as ANN offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems and fast calculation. In this investigation adaptive neuro-fuzzy inference system (ANFIS), which is a specific type of the ANN family, was used to predict the wake added turbulence. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS). This intelligent algorithm is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.