  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.
