The new energy vehicle is a robust measure to solve the problem of global warming. However, the new energy vehicle generally has the disadvantages of short mileage and difficulty in finding public chargers. The combination of wind energy harvest and new energy vehicle can be conducive to the promotion of the new energy vehicle. This paper proposes a novel adjustable Savonius vertical axis wind turbine (SVAWT). It contains three parts: an energy absorption module, an energy recovery module, and an energy conversion module. The energy absorption module includes four blades with staggered distribution in two layers. The overlap ratio of the blades can be adjusted by the wind speed, which can ensure the SVAWT has a higher energy transfer efficiency. The energy recovery module adjusts the overlap ratio of the blades without interruption by utilizing the self-rotation and the orbital revolution of the gears. The energy conversion module converts mechanical energy into electric energy and supplies power for the vehicle after adjustment by the voltage regulator module. Based on actual operating data, it can be found that the variation trend of power of the blades absorbing is consistent with wind speed and increases with the wind speed. Under four actual operating conditions, the root mean square value of the blades absorbing power are 7.0 W, 7.1 W, 3.9 W, and 5.1 W, respectively. These results reveal that the proposed novel adjustable SVAWT has high recovery power potential and can provide a valuable solution to the practical applications of wind energy harvesting.

Aerial photography using unmanned aerial vehicles (UAVs) to detect foreign objects is an important method to ensure the safety of transmission lines. However, existing detection algorithms often encounter challenges in complex environments, including limited recognition capability and high computational demands. To address these issues, this paper proposes YOLO-LAF, a lightweight foreign object detection algorithm that is based on YOLOv8n and incorporates an innovative adaptive weight pooling technique. The proposed method introduces a novel adaptive weight pooling module within the backbone network to enhance feature extraction for detecting foreign objects on transmission lines. Additionally, a multi-scale detection strategy is designed to integrate the FasterBlock and EMA modules. This combination enables the model to effectively capture both global and local image features through cross-channel interactions, thereby reducing misdetection and omission rates. Furthermore, a C2f-SCConv module is introduced in the neck network to streamline the model by eliminating redundant features, thus improving computational efficiency. Experimental results demonstrate that YOLO-LAF achieves average accuracies of 91.2% and 85.3% on the Southern Power Grid and RailFOD23 datasets, respectively, outperforming the original YOLOv8n algorithm by 2.6% and 1.8%. Moreover, YOLO-LAF reduces the number of parameters by 23.5% and 14.8% and the computational costs by 19.9% and 24.8%, respectively. These improvements demonstrate the superior detection performance of YOLO-LAF compared to other mainstream detection algorithms.