Optimization of an Airborne Wind Energy system using constrained Gaussian Processes
Optimization of an Airborne Wind Energy system using constrained Gaussian Processes
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使用约束高斯过程优化机载风能系统
DOI：
10.1109/cca.2014.6981519
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
C. Jones
中科院分区：
文献类型：
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作者：
S. Diwale;Ioannis Lymperopoulos;C. Jones
风力资源往往明显更强，并与海拔高度的增加更加一致。这种效应创造了发电的潜力，可以通过位于超过传统风力涡轮机高度的海拔高度的空中风能系统来获得。这种系统的一种常见设计包括一个拴在地面站上的飞行机翼。该电站可配备发电机，或安装在海上船舶上。我们展示了一个基于数据的方法，可以最大限度地提高这样一个系统的牵引力，通过优化低级别的跟踪控制器在约束的存在。我们利用高斯过程来学习从控制器的设定点到目标和约束函数的映射。然后，我们制定了一个机会约束优化问题，考虑到不确定性的学习功能。将概率目标函数转换为确定性获取函数，该确定性获取函数指示具有改进当前最优的高概率的设定点，并且在高不确定性区域中惩罚约束函数以确保可行性。仿真研究表明，我们可以找到最佳的控制器设置点，而不使用显着的假设模型动态，同时尊重未知的约束函数。
Wind resources tend to be significantly stronger and more consistent with increasing altitude. This effect creates a potential for power generation that can be reaped by an Airborne Wind Energy system positioned at elevations exceeding the height of conventional wind turbines. A frequent design for such a system includes a flying airfoil tethered to a ground station. The station can be equipped with a power generator or for the application considered here mounted to a sea vessel. We demonstrate a data based method that can maximize the towing force of such a system by optimizing a low level tracking controller at the presence of constraints. We utilise Gaussian Processes to learn the mapping from the set points of the controller to both the objective and the constraint function. We then formulate a chance - constrained optimization problem that takes into consideration uncertainty in the learned functions. The probabilistic objective function is transformed into a deterministic acquisition function which indicates set points with high probability of improving the current optimum and the constraint function is penalized in regions of high uncertainty to ensure feasibility. Simulation studies show that we can find optimal set points for the controller without the use of significant assumptions on model dynamics while respecting the unknown constraint function.