LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Short-term wind speed prediction using Extended Kalman filter and machine learning

Photo from wikipedia

Abstract Wind speed prediction could play an important role in improving the performance of wind turbine control and condition monitoring. For example, by predicting or forecasting the upcoming wind in… Click to show full abstract

Abstract Wind speed prediction could play an important role in improving the performance of wind turbine control and condition monitoring. For example, by predicting or forecasting the upcoming wind in advance, fluctuations in wind power output in above rated wind speed could be reduced without causing an increase in pitch activity, and anomalies such as an extreme gust could be detected before it reaches the wind turbine, allowing appropriate control actions to take place to minimise any potential damage that could be incurred by the anomalies. A novel wind speed prediction scheme is presented in this paper that comprises mainly two stages, estimation and prediction. Estimation is first carried out using an Extended Kalman filter, which is designed based on a 3 dimensional wind field model and a nonlinear rotor model. Prediction is subsequently performed in two steps, extrapolation and machine learning. The wind speed prediction scheme is tested using data obtained from a high-fidelity aeroelastic model.

Keywords: speed prediction; wind speed; extended kalman; using extended; prediction

Journal Title: Energy Reports
Year Published: 2020

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.