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A wind turbine operating in the wake of another turbine and has a reduced power production because of a lower wind speed after rotor. The flow field in the wake behind the first row turbines is characterized by a significant deficit in wind velocity and increased levels of turbulence intensity. To maximize the wind farm net profit, the number of turbines installed in the wind farm should be different in depend on wind farm project investment parameters. Therefore modeling wake effect is necessary because it has a great influence on the actual energy output of a wind farm. In this paper, the extreme learning machine (ELM) coupled with wavelet transform (ELM-WAVELET) is used for the prediction of wind turbine wake effect in wind far. Estimation and prediction results of ELM-WAVELET model are compared with the ELM, genetic programming (GP), support vector machine (SVM) and artificial neural network (ANN) models. The following error and correlation functions are applied to evaluate the proposed models: Root Mean Square Error (RMSE), Coefficient of Determination ( R 2 ) and Pearson coefficient ( r ). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by ELM-WAVELET approach (RMSE = 0.269) in comparison with the ELM (RMSE = 0.27), SVM (RMSE = 0.432), ANN (RMSE = 0.432) and GP model (RMSE = 0.433). [ABSTRACT FROM AUTHOR]

Production losses and increased turbine loadings are observed in wind farms, when wind turbines interact with each other. If a wind turbine is located in the wake of another one, its incoming flow is disturbed, slowed down, and its potential wind power is decreased. It is therefore necessary to study the wind turbine wakes and their interactions. It is important to consider these wake effects in the design of a wind farm in order to maximize the energy output and lifetime of the machines. The exact modeling of the wind speed distribution within a wind park is a fairly complicated task and many of the necessary parameters are not routinely available. A large number of studies have been established concerning the calculation of wake effect. Even though a number of mathematical functions have been proposed, 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 power deficit. 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. [ABSTRACT FROM AUTHOR]