Implementation of an Advanced Operation Control for AI-based Wind Farm Power Maximization Using Wake Redirection and Artificial Neural Networks

Abstract

Advanced operation control methods for wind power plants aim to increase the total power production or reduce structural loads on wind turbine components. The wake redirection control is used to increase the power production by adjusting the yaw angles of all wind turbines depending on the current wind conditions and the wind farm layout. The challenge for deploying the wake redirection control is to determine yaw angle set points that yield the optimal power production under varying wind conditions. In the project SmartWind, an active wake control is being implemented that uses AI-based methods to predict the impact of a large number of set point combinations on the power production of the wind farm. Its implementation shall be presented in this paper, including the AI algorithms and the set point scenario generation based on geometric considerations of the wind farms’ spatial layout. Simulation results using real wind farm parameters are used for validation. In the simulations, the advanced operation control increases the total power production by 2.1 % compared to the standard maximum power point tracking method and the wind field is used to explain the decision-making process. Finally, further optimizations to the operation control algorithm are proposed and the next steps towards the implementation in a real wind farm are illustrated.