Wind turbine power curve modeling using maximum likelihood estimation method

By Seok-Ho Seo, Si-Doek Oh, Ho-Young Kwak. Renewable Energy, 2019

58 citations recorded by the source corpus.

Modeling of wind turbine power curve which shows the relationship between wind speed and its power output can be used as an important tool in monitoring and forecasting wind energy. A data-driven approach to find most probable probability distribution function (PDF) for wind speed and turbine power is presented in this study. Equations for the scale and shape parameters in the Weibull wind speed distribution and equations for the four parameters in the logistic function were obtained explicitly by maximum likelihood estimation (MLE) method. With help of a selected data set from the wind speed and the corresponding power output data which was collected over a period of a year, the values of the parameters were obtained by solving the equations by iteration procedures. The predicted powers by the obtained logistic function closely follow the measured turbine powers averaged at 5-min or 10-min. Monitoring turbine power output by the logistic function was also tested for the measured powers in other time duration.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/6a78b03216173063b05978f12e308261dc64e4d6

Citations in the corpus

• Maximum likelihood hierarchical least squares‐based iterative identification for dual‐rate stochastic systems (2020, unknown): “Replacing t with th in Equations (7) and (8), we have the following dual-rate identification model: y(th) = φT(th)τ + ψT(th)ρ + v(th) (9) = φT(th)θ + v(th), (10)”