  Transient stability assessment (TSA) has always been a fundamental means for
ensuring the secure and stable operation of power systems. Due to the
integration of new elements such as power electronics, electric vehicles and
renewable power generations, dynamic characteristics of power systems are
becoming more and more complex, which makes TSA an increasingly urgent task.
Since traditional time-domain simulations and direct method cannot meet the
actual operation requirements of power systems, data-driven TSA has attracted
growing attention from both academia and industry. This paper makes a
comprehensive review from the following four aspects: feature extraction and
selection, model construction, online learning and rule extraction; and then,
summarizes the challenges and prospects for future research; finally, draws the
conclusions of this review. This review will be beneficial for relevant
researchers to better understand the research status, key technologies and
existing challenges in the field.
