Platform motions induced by waves pose a challenge to accurately predict the aerodynamic performance of floating offshore wind turbines (FOWTs). In view of this, the power performance and wake structure of FOWTs under platform pitch, surge, and their combined motions were investigated in this paper, using the computational fluid dynamics software, STAR-CCM+, with overset meshing and rigid body motion techniques. First, the simulation cases in single and same-phase combined motions with different amplitudes and frequencies were performed. Afterward, the approach of calculating the phase difference between pitch and surge motions was proposed to investigate the influence of the combined motion with phase difference on the aerodynamic performance. Results show that the increment of amplitude and frequency augments the mean power output and aggravates the power fluctuation in single and same-phase combined motions. The intensity of power variation under combined motion with a phase difference is weakened at 0.1 Hz compared to the single motion, while enhanced at 0.2 Hz, showing a different influence law on the aerodynamic performance. In addition, this paper established the power fluctuation table based on real sea states of Shidao in China, providing a certain reference for the controller design in this sea area.

Many drivetrain testing facilities have been built to reproduce multi-degree-of-freedom loads, thus simulating real wind conditions for evaluations of the reliability and durability of turbine subsystems. In this paper, the electrohydraulic schemes for the non-torque loading of a wind turbine’s drivetrain test benches are first analyzed. To deal with the control inaccuracy caused by the drastically increasing loading force, along with the rapid development of large-scale wind turbines, a multi-cylinder electrohydraulic digital-servo loading (MEDSL) technology is proposed. A novel electrohydraulic digital-servo cylinders group is designed. The proposed MEDSL can provide continuous and accurate load recurrence under wider wind conditions by varying the operational area of the cylinders group. Moreover, a sliding mode controller (SMC) is designed to realize the large dynamic loading of the MEDSL system. By comparing the SMC to a traditional PID controller in a servo-valve controlled cylinder, both simulation and experiment results proved the advantage of the proposed SMC. Accordingly, extensive experiments with a 4-cylinder case were carried out on a real full-loading bench using the SMC-based MEDSL device. The excellent tracking performance under complicated signals that represent the real wind loads demonstrated the feasibility and effectiveness of the proposed MEDSL technology and the SMC method.

Real-time Unmanned Aerial Vehicle (UAV) detection is a growing research field centered on advanced computer vision and deep learning algorithms. However, the rise of unmanned aerial vehicles (UAVs) has sparked numerous concerns due to their potential for malicious use in illegal activities. To address these concerns, Vision-based object detection approaches for UAVs have recently been developed. Nonetheless, UAV detection in real-world scenarios, such as images with diverse backgrounds and various perspectives, remains underexplored. To fill this gap, we present a new UAV detection dataset called the real-world scenarios dataset (RWSD). This dataset leverages real-world footage and is constructed under challenging conditions, including complex backgrounds, varying UAV sizes, different perspectives, and multiple UAV types. It aims to support the development of robust UAV detection algorithms that can perform well in diverse and realistic conditions. YOLO, a popular one-stage object detection approach, is widely employed for UAV detection across different environments due to its efficiency and simplicity. However, this series of detectors encounters challenges in real-world scenarios, such as excessive computation and suboptimal detection rates. In this study, we propose a lightweight feature enhancement model (LFEM) to address these limitations. Specifically, we base our model on YOLOv5, introducing the Ghost module to improve UAV detection with fewer floating-point operations (FLOPs). Additionally, we incorporate the SIMAM module to enhance feature representation, particularly for real-world scenarios. Extensive experiments on the RWSD, UAVDT, and DOTAv1.0 datasets demonstrate the effectiveness of our approach. Our proposed LFEM achieves an impressive 93.2% mAP₅₀, outperforming baseline models while maintaining a lightweight profile. Comparative and ablation studies further confirm that our algorithm is a promising and efficient solution for practical UAV detection tasks.

The dramatic expansion of wind turbines sets higher demands on the drivetrain test bench. The loading system of the drivetrain test bench should accurately reproduce real wind loads on the ground to evaluate the reliability of turbine components. In this paper, the multicylinder electrohydraulic digital-servo loading technology is applied to the non-torque loading system. The hydraulic cylinders groups are designed to substitute the original loading points. The loading center of each cylinders group is deviated due to the installation distance and the changed force distribution. Thus, a novel loading principle is proposed to compensate for the eccentric moment. The loading formula should be recalculated in realtime to ensure the implementation of accurate reproduction of 5-DOF loads based on the integration of multiple hydraulic cylinders groups. In addition, there are reverse sudden changes caused by the asynchronous opening and closing characteristics of the servo valve and the onoff valve. An advanced model-based sliding mode controller compensation strategy is proposed to achieve smooth switching. A mathematical analytical model is derived to deal with the parametric uncertainties and nonlinearities. Moreover, the extensive AMESim-Simulink co-simulations under various signals that represent the real wind loads have been conducted to investigate the performance of the proposed control strategy. By comparing the advanced controller to a traditional PID controller, the simulation results showed that the switching error was greatly reduced and the total loading process was smoother for the model-based sliding mode controller compensation strategy. The excellent tracking performance proved the feasibility and effectiveness of the proposed strategy.