Spatiotemporal Optimization for Vertical Path Planning of an Ocean Current Turbine
Spatiotemporal Optimization for Vertical Path Planning of an Ocean Current Turbine
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DOI：
10.1109/tcst.2022.3193637
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发表时间：
2023-03
影响因子：
4.8
通讯作者：
Arezoo Hasankhani;Yufei Tang;James H. VanZwieten;C. Sultan
中科院分区：
文献类型：
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作者：
Arezoo Hasankhani;Yufei Tang;James H. VanZwieten;C. Sultan
本文提出了一种新的时空优化方法，用于垂直路径规划（即航路点优化），以最大限度地提高海流涡轮机（OCT）在不确定海洋速度下的净输出功率。为了确定净功率，对水动能OCT发电和控制深度的功耗进行了建模。海洋速度的随机行为是空间和时间参数的函数，并通过高斯过程（GP）方法建模。采用基于模型的模型预测控制（MPC）和基于学习的强化学习（RL）两种不同的算法来解决带约束的规式化时空优化问题。对比研究表明，基于MPC和rl的方法在计算上是可行的，可以解决垂直路径规划问题，并使用基线a *方法进行评估。在速度预测不准确的情况下，进一步进行了鲁棒性分析。结果验证了所提出的方法在寻找最佳路径以最大化OCT系统总功率方面的效率，其中200h后的总利用能量比未优化的情况增加了18%以上。
This article presents a novel spatiotemporal optimization approach for vertical path planning (i.e., waypoint optimization) to maximize the net output power of an ocean current turbine (OCT) under uncertain ocean velocities. To determine the net power, OCT power generation from hydrokinetic energy and the power consumption for controlling the depth are modeled. The stochastic behavior of ocean velocities is a function of spatial and temporal parameters, which is modeled through a Gaussian process (GP) approach. Two different algorithms, including model predictive control (MPC) as a model-based method and reinforcement learning (RL) as a learning-based method, are applied to solve the formulated spatiotemporal optimization problem with constraints. Comparative studies show that the MPC- and RL-based methods are computationally feasible to address vertical path planning, which are evaluated with a baseline A* approach. Analysis of the robustness is further carried out under the inaccurate ocean velocity predictions. Results verify the efficiency of the presented methods in finding the optimal path to maximize the total power of an OCT system, where the total harnessed energy after 200 h shows over an 18% increase compared to the case without optimization.