A Sparse ANN-based Fatigue Estimation for Wind Turbine Control based on NMPC
Paper in proceedings, 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.

Author

Julio Alberto Luna Pacho

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Sébastien Gros

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Ole Falkenberg

IAV Automotive Engineering

Axe Schild

IAV Automotive Engineering

2018 European Control Conference, ECC 2018

398-403 8550167

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

Subject Categories

Probability Theory and Statistics

Control Engineering

Signal Processing

DOI

10.23919/ECC.2018.8550167

More information

Latest update

1/24/2019