Optimizing the total power output in a wind farm using long-short term memory

Abstract

The total energy obtained from wind farms is one of the topics researched by scientists to meet technological and daily needs. However, the wake phenomenon has an adverse impact on total power generation. It has also been observed to cause fatigue loads on the turbines in addition to reducing power generation. Therefore, in this study, efficient power generation was achieved by controlling the yaw angles of the wind turbines in the wind farm using a Long-Short Term Memory (LSTM) based approach. First, the flow field was defined using FLOW Redirection and Induction in Steady-state (FLORIS), and the wake phenomenon created by the wind turbines was analyzed. Total power output and wake formations at high and low wind velocities were investigated, and their suitability for the study was evaluated. Then, the wind direction prediction was performed using the LSTM algorithm. The specifics and performance of the wind direction prediction was examined and discussed using error metrics. Considering the predicted wind directions, the yaw angles of the turbines were adjusted over time, and a yaw control was implemented to obtain the efficient power output from the wind turbines. Furthermore, the equivalent simulations were conducted under different turbulence intensities, and the results were compared. Finally, the results obtained were interpreted. The aim of this study was to provide a foundational framework for researchers targeting efficiency from wind farms.