论文标题
基于PMU测量值Q学习的发电机参数估计
Generator Parameter Estimation by Q-Learning Based on PMU Measurements
论文作者
论文摘要
在本文中，提出了一种基于Q的新方法，用于使用PMU测量值估算同步发电机的参数。事件播放用于在不同参数下生成模型输出，以在Q学习中训练代理。我们假设模型中某些参数的确切值在Q学习中不知道。然后，计划制定依赖历史的最佳政策，以进行探索探索权衡。有了给定的先验知识，可以将参数向量视为具有特定奖励的状态，这是与测量值相比的拟合误差的函数。代理采取行动（增加或减少参数），并且估计的参数将移至新状态。根据奖励功能，最佳行动策略将将参数集移至具有最高奖励的状态。如果有多个事件可用，它们将被顺序使用，以便可以使用更新的$ \ MathBfcal {Q} $ - 值来提高计算效率。通过估计同步发生器的动态模型的参数来验证所提出的方法的有效性。
In this paper, a novel Q-learning based approach is proposed for estimating the parameters of synchronous generators using PMU measurements. Event playback is used to generate model outputs under different parameters for training the agent in Q-learning. We assume that the exact values of some parameters in the model are not known by the agent in Q-learning. Then, an optimal history-dependent policy for the exploration-exploitation trade-off is planned. With given prior knowledge, the parameter vector can be viewed as states with a specific reward, which is a function of the fitting error compared with the measurements. The agent takes an action (either increasing or decreasing the parameter) and the estimated parameter will move to a new state. Based on the reward function, the optimal action policy will move the parameter set to a state with the highest reward. If multiple events are available, they will be used sequentially so that the updated $\mathbfcal{Q}$-value can be utilized to improve the computational efficiency. The effectiveness of the proposed approach is validated by estimating the parameters of the dynamic model of a synchronous generator.
