In the case filtering link

as many stable and unstable cases as possible can be identified rapidly through qualitative machine learning or quantitative approximate analysis so that the number of cases requiring detailed analysis and the total computational burden can be reduced. This paper discusses the adopted assumptions

characteristic variables

classification rules and generalization ability of case filtering in transient stability analysis. The integration of data driven and model driven is analyzed

which includes the introduction of causal elements

the deep integration of data statistic paradigm and model simulation paradigm for knowledge extraction. Based on the stability mechanism

a two-layer classifier is proposed: the lower layer contains several parallel links

and each link utilizes quantitative algorithm with different degrees of approximation. Their output data are regarded as the input of the upper layer. According to the approximate causality

it can reflect the influence of most original data on transient stability. Therefore not only the correct recognition rate and robustness of the classifier can be both enhanced

but also the error mechanism

evaluation credibility and acceptability can be revealed.