Wind turbine power curve modeling based on Gaussian Processes and Artificial Neural Networks

Abstract An accurate estimation of the wind turbine power curve is a key issue to the provision of the electricity that the wind farm will transfer to the grid and for a correct evaluation of the performance of each turbine. Artificial Neural Networks (ANN) have proven to be well suited for solving this problem. We propose in this study that including a prior automatic filtering, by means of Gaussian Processes modelling, improves the network performance significantly, and saves substantial time and resources. The aim of this paper is to present a complete method based on Gaussian Processes data pre-filtering and ANN modeling of wind turbine power curves. Results show that this procedure improves the standard ANN modeling and also improves on the widely used IEC-61400 standard (International Electro technical Commission) and other parametric and non-parametric methods. Overall, there is a significant improvement (25%) in root mean square error of predicted power using our proposed model compared to standard methods currently applied.

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