论文标题
深度积极学习能力系统中的可溶性预测
Deep Active Learning for Solvability Prediction in Power Systems
论文作者
论文摘要
传统的可溶性区域分析方法只能具有不确定的保守主义的内部近似值。已经提出了机器学习方法来接近现实区域。在这封信中，我们为电力系统的可溶性预测提供了一个深厚的积极学习框架。与在标记所有实例后执行培训的被动学习方法相比，主动学习选择了最有用的实例以标记，因此大大减少了用于培训的标签数据集的大小。在主动学习框架中，与分类器的直接后验概率一起定义了与不同的采样策略相对应的采集函数。使用IEEE 39-BUS系统来验证所提出的框架，其中说明了二维情况以可视化采样方法的有效性，然后是全维数值实验。
Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism. Machine learning methods have been proposed to approach the real region. In this letter, we propose a deep active learning framework for power system solvability prediction. Compared with the passive learning methods where the training is performed after all instances are labeled, the active learning selects most informative instances to be label and therefore significantly reduce the size of labeled dataset for training. In the active learning framework, the acquisition functions, which correspond to different sampling strategies, are defined in terms of the on-the-fly posterior probability from the classifier. The IEEE 39-bus system is employed to validate the proposed framework, where a two-dimensional case is illustrated to visualize the effectiveness of the sampling method followed by the full-dimensional numerical experiments.
