Abstract：
Harvesting wind energy from the environment and integrating it with the internet of things and artificial intelligence to enable intelligent ocean environment monitoring are effective approach. There are some challenges that limit the performance of wind energy harvesters, such as the larger start-up torque and the narrow operational wind speed range. To address these issues, this paper proposes a wind energy harvesting system with a self-regulation strategy based on piezoelectric and electromagnetic effects to achieve state monitoring for unmanned surface vehicles (USVs). The proposed energy harvesting system comprises eight rotation units with centrifugal adaptation and four piezoelectric units with a magnetic coupling mechanism, which can further reduce the start-up torque and expand the wind speed range. The dynamic model of the energy harvester with the centrifugal effect is explored, and the corresponding structural parameters are analyzed. The simulation and experimental results show that it can obtain a maximum average power of 23.25 mW at a wind speed of 8 m/s. Furthermore, three different magnet configurations are investigated, and the optimal configuration can effectively decrease the resistance torque by 91.25% compared with the traditional mode. A prototype is manufactured, and the test result shows that it can charge a 2200 μF supercapacitor to 6.2 V within 120 s, which indicates that it has a great potential to achieve the self-powered low-power sensors. Finally, a deep learning algorithm is applied to detect the stability of the operation, and the average accuracy reached 95.33%, which validates the feasibility of the state monitoring of USVs.
摘要：
从环境中收集风能，并将其与物联网和人工智能相结合，以实现智能海洋环境监测是有效的方法。有一些挑战限制了风能采集器的性能，如较大的启动转矩和狭窄的运行风速范围。为了解决这些问题,本文提出了一种具有基于压电和电磁效应的自我调节策略的风能收集系统，以实现对无人表面车辆（USV）的状态监测。所提出的能量收集系统包括八个具有离心适应的旋转单元和四个具有磁耦合机制的压电单元。这可以进一步降低启动扭矩，扩大风速范围。探索了具有离心效应的能量采集器的动力学模型,并对相应的结构参数进行了分析。仿真和实验结果表明，在风速为8m/s时，它可以获得最大平均功率为23.25mW。此外，研究了三种不同的磁体配置，优化配置可有效降低阻力矩91.25%。制造了一个原型，测试结果表明，它可以在120s内将2200μF的超级电容器充电到6.2V，这表明它具有实现自供电低功耗传感器的巨大潜力。最后,应用深度学习算法来检测操作的稳定性，平均准确率达到95.33%,验证了USV状态监控的可行性。
