A Sparse ANN-based Fatigue Estimation for Wind Turbine Control based on NMPC
Paper i proceeding, 2018

In this paper, an Artificial Neural Network (ANN) methodology for estimating the tower fatigue of a wind turbine (WT) is developed. Specifically, the fatigue is estimated using an ANN that receives frequency-domain measurements of the tower base position and velocity. The frequencies are selected using an algorithm that detects the most relevant values and generates a sampling grid. The complexity of the ANN-based fatigue estimation is analysed to study the viability of its deployment in a real-time Nonlinear Model Predictive Control (NMPC) formulation.

Författare

Julio Alberto Luna Pacho

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Sébastien Gros

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Ole Falkenberg

Ingenieurgesellschaft für Auto und Verkehr GmbH (IAV)

Axe Schild

Ingenieurgesellschaft für Auto und Verkehr GmbH (IAV)

2018 European Control Conference, ECC 2018

398-403 8550167

16th European Control Conference, ECC 2018
Limassol, Cyprus,

Ämneskategorier

Sannolikhetsteori och statistik

Reglerteknik

Signalbehandling

DOI

10.23919/ECC.2018.8550167

Mer information

Senast uppdaterat

2019-01-24