For the autonomous drone-based inspection of wind turbine (WT) blades, accurate detection of the WT and its key features is essential for safe drone positioning and collision avoidance. Existing deep learning methods typically rely on manually labeled real-world images, which limits both the quantity and the diversity of training datasets in terms of weather conditions, lighting, turbine types, and image complexity. In this paper, we propose a method to generate synthetic training data that allows controlled variation of visual and environmental factors, increasing the diversity and hence creating challenging learning scenarios. Furthermore, we train a YOLOv11 feature detection network solely on synthetic WT images with a modified loss function, to detect WTs and their key features within an image. The resulting network is evaluated both using synthetic images and a set of real-world WT images and shows promising performance across both synthetic and real-world data, achieving a Pose mAP50-95 of 0.97 on real images never seen during training.