Review on Optimal Wind Farm Control Techniques and Prospects of Artificial Intelligence

This paper presents a review based on Axial Induction Control (AIC), Wake Redirection Control (WRC) and artificial intelligence to support operation and optimal control of a wind farm. In addition to that, a simulation is carried out using Sim Windfarm in Matlab/Simulink, to generate a wind farm model with 8 wind turbines based on the NREL 5MW reference turbine. The data generated from the model consists of 24 measured values of wind speed and total power production by the wind farm. The main objective of the simulations is to generate data that can be used to predict the power output of the wind farm. The power prediction is done using nonlinear auto-regressive network with exogenous inputs (NARX) neural network, in which the measured wind speed is used as input to the NARX network, while power production is used as a target. The NARX configuration is based on a two-layer feed-forward network, with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer. The NARX network performs a one step ahead prediction for 1.25 seconds with small error. The prediction result indicates a good performance as confirmed by the error autocorrelation plot, regression graph and the response plot generated by the network. This prediction can be used to improve WRC, AIC, or turbines control in the range of seconds.