Artificial Neural Network-based Wake Steering Control under the Time-varying Inflow

The wind farm power loss due to wake interaction becomes more pronounced with a more compact farm layout. The steady-state wake model, for example, the Gaussian wake model, can help optimize the wake deflection and reduce the power loss. However, accurately estimating the farm-level ambient wind, like wind speed, direction, and turbulence intensity, is challenging, especially under time-varying inflow conditions. An artificial neural network-based (ANN) wind farm control strategy is proposed for real-time wake steering control. This algorithm only requires the measurement data of each turbine without estimating the farm’s ambient wind. Firstly, numerous steady-state simulation scenarios are designed by setting different ambient winds and yaw deflection. Then, these simulation measurement data and optimal yaw offsets are used to train the neural network as the farm control strategy. This method utilizes the nonlinear mapping capabilities and knowledge compression characteristics of neural networks. Finally, the effectiveness of the control strategy is validated in a dynamic simulation setting, which includes fluctuating wind direction in the inflow.