A Deep Learning Solution to optimise the control of Tidal Stream Conversion Devices with considerations of the marine environment

该博士项目旨在研究一种在使用潮流涡轮机时保护海洋生态系统的深度学习解决方案。潮流涡轮机具有产生大量能量的潜力，全球潮流能量潜力估计约为120-400千兆瓦。潮汐涡轮机本身就是将海流中的能量转化为有用能量的装置，其方式与风力涡轮机几乎相同。然而，潮汐涡轮机可能会对海洋生态系统产生重大影响，既有与海洋物种发生碰撞的风险，也有产生的作业噪音水平。潮汐涡轮机不仅对环境有影响，附近野生动物造成的尾迹分散也会影响涡轮机的功率输出。因此，控制系统必须到位，以保持涡轮机在最佳功率输出下工作。在这个项目中，深度学习将通过使用涡轮机传感器捕获的信息(如功率、推力、角速度和流速)来检测海洋生物多样性的存在，以避免在大规模部署之前危及海洋生态系统。因此，本博士的目标将是对海洋生物的水流扰动如何影响潮汐涡轮机阵列进行分类和数值量化，然后设计深度学习体系结构，在碰撞发生之前检测海洋生物的存在并相应地控制其运行。

This PhD project aims to investigate a deep-learning solution for preserving the marine ecosystem when using tidal stream turbines. Tidal stream turbines have the potential to generate a significant amount of energy, with the global tidal stream energy potential estimated to be around 120-400 GW. Tidal turbines themselves are devices that convert the energy in marine currents into useful energy in a nearly identical manner to wind turbines. However, tidal turbines may have a significant impact on the marine ecosystem, both through the risk of collision with marine species and the level of operational noise generated. Not only do tidal turbines have an effect on the environment, but the wake dispersion caused by nearby wildlife can also affect the power output of the turbine. As such, control systems have to be in place in order to keep the turbine working at an optimal power output. In this project, deep learning will be used to detect the presence of marine biodiversity by using information captured by the turbine's sensors, such as power, thrust, angular velocity, and flow speed, to avoid endangering the marine ecosystem prior to large-scale deployment. Therefore, the aim of this PhD will be to classify and numerically quantify how flow disturbances from marine life affect tidal turbine arrays, and then devise deep-learning architectures to detect marine life's presence before a collision occurs and control its operation accordingly.