Super-short term wind speed prediction based on artificial neural networks for wind turbine control applications
Paper i proceeding, 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.

Författare

Julio Alberto Luna Pacho

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Rafal Noga

Ingenieurgesellschaft für Auto und Verkehr GmbH (IAV)

Sebastien Gros

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Jens Geisler

Senvion S.A.

Axel Schild

Ingenieurgesellschaft für Auto und Verkehr GmbH (IAV)

Ole Falkenberg

Ingenieurgesellschaft für Auto und Verkehr GmbH (IAV)

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,

Ämneskategorier

Reglerteknik

Signalbehandling

Annan elektroteknik och elektronik

DOI

10.1109/IECON.2018.8591623

Mer information

Senast uppdaterat

2019-06-11