LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:

OpenAIRE is about to release its new face with lots of new content and services.
During September, you may notice downtime in services, while some functionalities (e.g. user registration, login, validation, claiming) will be temporarily disabled.
We apologize for the inconvenience, please stay tuned!
For further information please contact helpdesk[at]openaire.eu

fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Knudsen, Torben (2014)
Languages: English
Types: Book
Subjects: Wind energy, Dynamic inflow, State estimation, Kalman filter, Unscented transform, KF, EKF, UKF
Dynamic inflow is an effect which is normally not included in the models used for wind turbine control design. Therefore, potential improvement from including this effect exists. The objective in this project is to improve the methods previously developed for this and especially to verify the results using full-scale wind turbine data. The previously developed methods were based on extended Kalman filtering. This method has several drawback compared to unscented Kalman filtering which has therefore been developed. The unscented Kalman filter was first tested on linear and non-linear test cases which was successful. Then the estimation of a wind turbine state including dynamic inflow was tested on a simulated NREL 5MW turbine was performed. This worked perfectly with wind speeds from low to nominal wind speed as the output prediction errors where white. In high wind where the pitch actuator was always active the results where not as convincing because the output prediction errors where not white. Using real data it has not been possible to get really good results so far. There remains a number of challenges: verifying turbine parameters and getting the most suitable measurement signals, including the 3P effect in the model and perhaps including the 1P effect. It is obviously difficult to make a final conclusion before the above challenges has been resolved.
  • No references.
  • No related research data.
  • No similar publications.

Share - Bookmark

Download from

Cite this article

Collected from

Cookies make it easier for us to provide you with our services. With the usage of our services you permit us to use cookies.
More information Ok