This paper presents an intelligent wind speed sensor less maximum power point tracking (MPPT) method for a variable speed wind energy conversion system (VS-WECS) based on a Q-Learning algorithm. The Q-Learning algorithm consists of Q-values for each state action pair which is updated using reward and learning rate. Inputs to define these states are electrical power received by grid and rotational speed of the generator. In this paper, Q-Learning is equipped with peak detection technique, which drives the system towards peak power even if learning is incomplete which makes the real time tracking faster. To make the learning uniform, each state has its separate learning parameter instead of common learning parameter for all states as is the case in conventional Q-Learning. Therefore, if half learned system is running at peak point, it does not affect the learning of unvisited states. Also, wind speed change detection is combined with proposed algorithm which makes it eligible to work for varying wind speed conditions. In addition, the information of wind turbine characteristics and wind speed measurement is not needed. The algorithm is verified through simulations and experimentation and also compared with perturbation and observation (P&O) algorithm.

中文翻译：

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基于Q-Learning的变风速风能转换系统最大功率提取

本文提出了一种基于 Q-Learning 算法的变速风能转换系统 (VS-WECS) 的智能风速传感器无最大功率点跟踪 (MPPT) 方法。Q-Learning 算法由使用奖励和学习率更新的每个状态动作对的 Q 值组成。定义这些状态的输入是电网接收的电力和发电机的转速。在本文中，Q-Learning 配备了峰值检测技术，即使学习不完整，也可以将系统驱动到峰值功率，从而使实时跟踪更快。为了使学习统一，每个状态都有其单独的学习参数，而不是传统 Q-Learning 中所有状态的通用学习参数。因此，如果半学习系统在峰值点运行，它不会影响未访问国家的学习。此外，风速变化检测与提出的算法相结合，使其有资格在不同的风速条件下工作。此外，不需要风力涡轮机特性和风速测量的信息。该算法通过仿真和实验得到验证，并与扰动和观察（P&O）算法进行了比较。