Wind turbine power curve modeling using radial basis function neural networks and tabu search

Wind turbine power curve (WTPC) modeling is of great importance for performance monitoring. This work proposes a new method for producing highly accurate non-parametric models for wind turbines based on artificial neural networks (ANNs). To achieve this, we employ networks belonging to the radial basis function (RBF) architecture, and feed them with additional important input variables besides wind speed. To further increase modeling accuracy, while at the same time keeping the computational cost at acceptable levels, we introduce a new training algorithm based on the successful non-symmetric fuzzy means (NSFM) approach, which in this work is hybridized with the tabu search (TS) metaheuristic technique, enabling the method to train efficiently datasets of high dimensionality. The resulting method is evaluated on real data from four wind turbines, whereas a comparison with numerous WTPC modeling schemes, including parametric and non-parametric models is conducted. The solution found by the proposed algorithm outperforms the results produced by its rivals in terms of both modeling accuracy and efficiency, while in most cases it also leads to simpler models. The resulting models can be used successfully, not only for accurate WTPC modeling, but also for constructing wind turbine performance analysis tools, e.g. 3-D power curves.