Super-short term wind speed prediction based on artificial neural networks for wind turbine control applications
Paper in proceedings, 2018

In this paper, an Artificial Neural Network (ANN) methodology to cast super-short term (under 30 seconds) wind speed predictions is presented. The aim is to obtain computationally efficient super-short term predictions that will be used in Wind Turbine (WT) real-time control applications in the future. A combination of power measurements and meteorological data are used to obtain the estimated rotor effective wind speed. This signal is then used as an input to train the ANNs. Additionally, a polynomial fitting is proposed to enhance the ANN results at each prediction step. The proposed strategy is compared with a classic persistence approach in order to quantify the achieved improvement.

Author

Julio Alberto Luna Pacho

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Rafal Noga

IAV Automotive Engineering

Sebastien Gros

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Jens Geisler

Senvion S.A.

Axel Schild

IAV Automotive Engineering

Ole Falkenberg

IAV Automotive Engineering

Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society

1952-1957 8591623

44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018
Washington, USA,

Subject Categories

Control Engineering

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/IECON.2018.8591623

More information

Latest update

6/11/2019