Wind Turbine Power Curve Modelling using Gaussian Mixture Copula, ANN Regressive and BANN

Abstract

In terms of monitoring wind turbine performance, the power curve plays a key role and it is a valuable indicator of the wind turbine’s condition as it allows the identification of anomalies related to production. On the other hand, by calculating a typical reference curve of the machine, it is possible to calculate losses and estimate its performance. A suitable model that characterises the power curve can benefit the estimated production of a wind turbine or wind farm. The most widely used models to characterise power curves are based on 2-variable models (wind speed and power). The power curve is affected by different exogenous variables (such as temperature, wind direction or turbulence intensity) that must be taken into account in the characterisation of the power curve. Consequently, the working conditions of the wind turbine are different depending on the site, and multivariable models that can take these variables into account are required. Currently new models and alternatives are emerging. This study will evaluate the performance of multivariate models based on Gaussian mixture copula model (GMCM), Regressive artificial neural Networks (ANN) and Bayesian artificial neural networks (BANN) in terms of power curve characterisation and power estimation. The results will be compared to the most common methods such as the manufacturer power curve and historical power curve, which is generated using the the bins method. Taking advantage of the benefits of neural networks (NN), particular types of NN have been considered to provide the value of uncertainty in the estimation of power.