Wind farm optimization by active yaw control strategy using machine learning approach

Recent advancements in wind farm (WF) construction have prompted designers to focus on optimizing WF performance and minimizing the negative effect of wake interference on power output. One proven technique to reduce wake influence and improve power generation is wake steering, which involves adjusting the yaw angles of wind turbines (WTs). The objective of this study is to evaluate the optimization of WF using machine learning (ML), specifically an artificial neural network (ANN) approach. In the first phase of the study, an ANN power model is selected to standardize wake loss and optimize power. In the next phase, a genetic algorithm (GA) will be employed to determine the best yaw angles for the wind direction based on data collected from the ANN power model. The ANN power model might not account for maintenance tasks around the WF. The results of the optimization demonstrate that energy is maximized between wind directions of 166° to 178°. This research achieved a .97 optimized overall power ratio, compared to non-optimized scenarios, using an optimal yaw angle technique in all directions. The findings of the study demonstrate that ANN-based optimization, combined with standardized wake degradation and suitable yaw angle direction, is an efficient method for WF optimization.