Performance enhancement of the artificial neural network–based reinforcement learning for wind turbine yaw control

Abstract

The yaw angle control of a wind turbine allows maximization of the power absorbed from the wind and, thus, the increment of the system efficiency. Conventionally, classical control algorithms have been used for the yaw angle control of wind turbines. Nevertheless, in recent years, advanced control strategies have been designed and implemented for this purpose. These advanced control strategies are considered to offer improved features in comparison to classical algorithms. In this paper, an advanced yaw control strategy based on reinforcement learning (RL) is designed and verified in simulation environment. The proposed RL algorithm considers multivariable states and actions, as well as the mechanical loads due to the yaw rotation of the wind turbine nacelle and rotor. Furthermore, a particle swarm optimization (PSO) and Pareto optimal front (PoF)-based algorithm have been developed in order to find the optimal actions that satisfy the compromise between the power gain and the mechanical loads due to the yaw rotation. Maximizing the power generation and minimizing the mechanical loads in the yaw bearings in an automatic way are the objectives of the proposed RL algorithm. The data of the matrices Q (s,a) of the RL algorithm are stored as continuous functions in an artificial neural network (ANN) avoiding any quantification problem. The NREL 5-MW reference wind turbine has been considered for the analysis, and real wind data from Salt Lake, Utah, have been used for the validation of the designed yaw control strategy via simulations with the aeroelastic code FAST.

Wind Turbine Operation Curves Modelling Techniques

“…As regards the latter point, actually, it should be noticed that the generator speedpower curve has been analyzed in the context of wind turbine aging analysis in [37]: despite the methodology employed in that study was simpler with respect to the analysis proposed in this work, it has been sufficient to highlight a considerable under-performance of a wind turbine because of generator efficiency aging. Another potential application of this kind of methodology is the analysis of wind turbine optimization technology, which likely intervenes with slight modifications of the characteristic operation curves of the wind turbines (see for example [42][43][44][45][46]), involving the rotational speed and-or the blade pitch control. A practical approach for assessing the net effect of this kind of technology upgrades is training a model (similar to those presented in this work) with data before the technology upgrade, validating on two target data sets (one before and one after the upgrade) and analyzing how the statistical properties of the residuals between model estimates and measurements change.…”

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

“…Deep learning provides a better way to solve some complex problems [24]. As the basis of deep learning, artificial neural network (ANN) has the ability to adapt to the characteristics of autonomous learning of complex problems and can deal with nonlinear problems well [25]. RBF neural network has good generalization ability and can approach nonlinear problems with unlimited accuracy [26][27][28].…”

Differential Evolution Optimal Parameters Tuning with Artificial Neural Network

“…An ANN consists of many interconnected simple functional units (neurons) that perform as parallel information-processors and approximate the function that maps inputs to outputs [33]. ANNs potential to solve problems with high performance and the ability to adapt to different problems have been implemented in numerous fields such as autonomous driving [34,35], solar and wind energy systems [36,37] and financial time series forecasting [38]. The field of ANNs is in full motion, in that way to review its progress and application areas in real-world scenarios, see Abiodun et al [39].…”