Wind turbine fatigue reduction based on economic-tracking NMPC with direct ANN fatigue estimation.

The aim of this work is to deploy an advanced Nonlinear Model Predictive Control (NMPC) approach for reducing the tower fatigue of a wind turbine (WT) tower while guaranteeing efficient energy extraction from the wind. To achieve this, different Artificial Neural Network (ANN) architectures are trained and tested in order to estimate the tower fatigue as a surrogate of the traditional Rainflow Counting (RFC) method. The ANNs receive data stemming from the tower top oscillation velocity and the previous fatigue state to directly estimate the fatigue progression. The results are compared to select the most convenient architecture for control implementation. Once an ANN is selected, an economic-tracking NMPC (etNMPC) solution to reduce the fatigue of the WT tower is deployed in real-time. The closed-loop results are then compared to a baseline controller from a renowned WT simulation tool and a classic etNMPC implementation with indirect fatigue minimisation to demonstrate the improvement achieved with the proposed strategy. Finally, conclusions regarding computational cost and real-time deployment capabilities are discussed, as well as future lines of research. • We study the effects of fatigue on a multi-megawatt wind turbine tower. • We use machine-learning to estimate the evolution of fatigue on the wind turbine tower. • A real-time economic-tracking NMPC to improve efficiency while reducing tower fatigue is proposed. • The designed controller is tested with data stemming from different wind speeds. • Comparison between the proposed strategy and the FAST controller. [ABSTRACT FROM AUTHOR]