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Comparison of advanced nonparametric models for wind turbine power curves

Pandit, Ravi and Infield, David and Kolios, Athanasios (2019) Comparison of advanced nonparametric models for wind turbine power curves. IET Renewable Power Generation. ISSN 1752-1416

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Abstract

To continuously assess the performance of a wind turbine, accurate power curve modelling is essential. Various statistical methods have been used to fit power curves to performance measurements; these are broadly classified into parametric and nonparametric methods. In this paper, three advanced nonparametric approaches, namely: Gaussian Process; Random Forest; and Support Vector Machine are assessed for wind turbine power curve modelling. The modelled power curves are constructed using historical wind turbine SCADA data obtained from operational three bladed pitch regulated wind turbines. The modelled power curve fitting performance is then compared using suitable performance error metrics to identify the most accurate approach. It is found that a power curve based on a Gaussian Process has the highest fitting accuracy, whereas the Support Vector Machine approach gives poorer but acceptable results, over a restricted wind speed range. Power curves based on a Gaussian Process or Support Vector Machine provide smooth and continuous curves, whereas power curves based on the Random Forest technique are neither smooth nor continuous. This paper highlights the strengths and weaknesses of the proposed nonparametric techniques to construct a robust fault detection algorithm for wind turbines based on power curves.