浮式海上风力发电机桨距控制的机器学习控制

Michael B. Kane
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引用次数: 13

摘要

由于效率低下和维护成本高,目前漂浮式海上风力涡轮机(fowt)的能源成本并不经济,留下了大量未开发的可再生能源。共同设计更轻、更便宜的fowt,每个叶片具有单独的螺距控制(IPC),可以提高效率、降低成本,并使海上风电在经济上可行。然而,非线性动力学和非平稳风浪载荷的宽度为每个期望的位置和情况设计有效和稳健的IPC提出了挑战。本文介绍了用于FOWTs IPC的机器学习控制(MLC)的开发,设计和仿真。MLC已被证明对许多复杂的非线性流固耦合问题是有效的。该项目研究将这些组件级控制问题扩展到NREL 5MW OC3 fot的系统级控制。利用MATLAB Simulink和OpenFAST开发了一种大规模并行遗传程序(GP),可在最具挑战性的设计负载情况下高效地评估新个体并选择性地测试每一代的适应度。使用成本函数将所提出的控制器与基线PID控制器进行比较,该成本函数捕获了年发电量的值,维护成本与极限负载和谐波疲劳相关。该控制器的成本仅为基准PID控制器的67%,在给定设计负载情况下,OC3 FOWT的IPC在ARPA-E ATLAS Offshore竞赛中获得第四名。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Control for Floating Offshore Wind Turbine Individual Blade Pitch Control
The cost of energy from current floating offshore wind turbines (FOWTs) are not economical due to inefficiencies and maintenance costs, leaving significant renewable energy resources untapped. Co-designing lighter less expensive FOWTs with individual pitch control (IPC) of each blade could increase efficiencies, decreases costs, and make offshore wind economically viable. However, the nonlinear dynamics and breadth of nonstationary wind and wave loading present challenges to designing effective and robust IPC for each desired location and situation.This manuscript presents the development, design, and simulation of machine learning control (MLC) for IPC of FOWTs. MLC has been shown effective for many complex nonlinear fluid-structure interaction problems. This project investigates scaling up these component-level control problems to the system level control of the NREL 5MW OC3 FOWT. A massively parallel genetic program (GP) is developed using MATLAB Simulink and OpenFAST that efficiently evaluates new individuals and selectively tests fitness of each generation in the most challenging design load case. The proposed controller was compared to a baseline PID controller using a cost function that captured the value of annual energy production with maintenance costs correlated to ultimate loads and harmonic fatigue. The proposed controller achieved 67% of the cost of the baseline PID controller, resulting in 4th place in the ARPA-E ATLAS Offshore competition for IPC of the OC3 FOWT for the given design load cases.
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