WTC-MobResNet: A Deep Learning Approach for Detecting Wind Turbine Clutter in Weather Radar Data

With the rapid expansion of Wind Parks (WPs), Wind Turbine Clutter (WTC) has become a significant challenge due to the interference it causes with data from next-generation Doppler weather radars. Traditional clutter detection methods struggle to strike a balance between detection accuracy and efficiency. This study proposes a deep learning model named WTC-MobResNet, which integrates the architectures of MobileNet and ResNet and is specifically designed for WTC detection tasks. The model combines the lightweight characteristics of MobileNet with the residual learning capabilities of ResNet, enabling efficient extraction of WTC features from weather radar echo data and achieving precise identification of WTC. The experimental results demonstrate that the proposed model achieves an ACC of 98.21%, a PRE of 97.52%, a POD of 98.99%, and an F1 score of 98.25%, outperforming several existing deep learning models in both detection accuracy and false alarm control. These results confirm the potential of WTC-MobResNet for real-world operational applications.