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中文摘要：
针对双馈感应风力发电系统结构复杂，受参数变化和外部干扰较显著，具有非线性、时变、强耦合的特。点，在传统矢量控制的基础上，提出一种基于强化学习的自校正控制方法。引入Q学习算法作为强化学习核心算法，快速自动地在线优化PI控制器的输出。基于MATLAB／Simulink环境，在风速低于额定风速时对系统进行仿真，结果表明，引入强化学习自校正控制后，保持了原系统最大风能捕获的能力，同时改善了其动态性能，增强了鲁棒性和自适应性。
英文摘要：
The doubly-fed induction wind power generator is a complex, strong coupling,time-varying and highly non- linear system, whose performances are easily affected by parameter changes and external disturbances. On the foundation of the traditional vector control, a self-tuning control method based on reinforcement learning （RL） theory was proposed. The Q-learning method was introduced as the core algorithm of reinforcement learning to optimize the control signals of the PI controller through rapidly automatic on-line learning. The simulation was conducted in MATLAB/Simulink when wind speed was under the rated value. The results show that the control system with an additional self-tuning controller maintains the a- bility to capture the maximum wind power, while improving its dynamic performance and enhancing the robustness and a- daptability, compared with the traditional vector control.
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