This paper analyses importance of including wind direction (WD) as an additional explanatory variable to the wind speed (WS) for evaluating uncertainty in wind turbine (WT) power output (Pout). Using available measurements of an actual WT, the paper compares a ‘two-dimensional’ (2D) Pout-WS model with a ‘three-dimensional’ (3D) Pout-WS-WD model for two general cases: (a) for the specific input WS and WD values (i.e. WS and WD without uncertainties), and (b) for the forecasted input WS and WD values (i.e. WS and WD with uncertainties). In paper, 2D and 3D Gaussian mixture Copula model and vine Copula framework are combined with 2D and 3D Markov chain models, which are used to forecast input WS and WD data with uncertainties. The obtained results show that inclusion of WD will provide noticeable improvement for models with no uncertainties in input WS and WD data, while in the case of forecasted WS and WD data with uncertainties, WS is a much stronger contributor to the total WT Pout uncertainty than WD.

Evaluation of wind turbine power outputs with and without uncertainties in input wind speed and wind direction data

Di Giorgio V.;Langella R.;Testa A.
2020

Abstract

This paper analyses importance of including wind direction (WD) as an additional explanatory variable to the wind speed (WS) for evaluating uncertainty in wind turbine (WT) power output (Pout). Using available measurements of an actual WT, the paper compares a ‘two-dimensional’ (2D) Pout-WS model with a ‘three-dimensional’ (3D) Pout-WS-WD model for two general cases: (a) for the specific input WS and WD values (i.e. WS and WD without uncertainties), and (b) for the forecasted input WS and WD values (i.e. WS and WD with uncertainties). In paper, 2D and 3D Gaussian mixture Copula model and vine Copula framework are combined with 2D and 3D Markov chain models, which are used to forecast input WS and WD data with uncertainties. The obtained results show that inclusion of WD will provide noticeable improvement for models with no uncertainties in input WS and WD data, while in the case of forecasted WS and WD data with uncertainties, WS is a much stronger contributor to the total WT Pout uncertainty than WD.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11591/466916
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