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To continuously assess the performance of a wind turbine (WT), 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 non-parametric methods. In this study, three advanced non-parametric approaches, namely: Gaussian Process (GP); Random Forest (RF); and Support Vector Machine (SVM) are assessed for WT power curve modelling. The modelled power curves are constructed using historical WT supervisory control and data acquisition, data obtained from operational three bladed pitch regulated WTs. 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 GP has the highest fitting accuracy, whereas the SVM approach gives poorer but acceptable results, over a restricted wind speed range. Power curves based on a GP or SVM provide smooth and continuous curves, whereas power curves based on the RF technique are neither smooth nor continuous. This study highlights the strengths and weaknesses of the proposed non-parametric techniques to construct a robust fault detection algorithm for WTs based on power curves.
- curve fitting
- decision tree
- fault detection
- parameter estimation
- support vector machines
- wind turbines
FingerprintDive into the research topics of 'Comparison of advanced non-parametric models for wind turbine power curves'. Together they form a unique fingerprint.
Comparative analysis of Gaussian Process power curve models based on different stationary covariance functions for the purpose of improving model accuracyPandit, R. K. & Infield, D., 30 Sep 2019, In: Renewable Energy. 140, p. 190-202 13 p.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile
Pandit, R. K. & Infield, D., 15 Mar 2019, In: The Journal of Engineering. p. 1-5 5 p.
Research output: Contribution to journal › Conference Contribution › peer-reviewOpen AccessFile16 Downloads (Pure)
Comparative analysis of binning and support vector regression for wind turbine rotor speed based power curve use in condition monitoringPandit, R. & Infield, D., 13 Dec 2018, 2018 53rd International Universities Power Engineering Conference (UPEC). Piscataway, NJ: IEEE, 6 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution bookOpen AccessFile