Data-driven methods are widely recognized and generate conducive results for online transient stability assessment. However

the tedious and time-consuming process of sample collection is often overlooked. The functioning of power systems involves repetitive sample collection due to the constant variations occurring in the operation mode

thereby highlighting the importance of collection efficiency. As a means to achieve high sample collection efficiency following the operation mode change

we propose a novel instance-transfer method based on compression and matching strategy

which facilitates the direct acquisition of useful previous samples

used for creating the new sample base. Additionally

we present a hybrid model to ensure rationality in the process of sample similarity comparison and selection

where features of analytical modeling with special significance are introduced into data-driven methods. At the same time

a data-driven method can also be integrated in the hybrid model to achieve rapid error correction of analytical models

enabling fast and accurate post-disturbance transient stability assessment. As a paradigm

we consider a scheme for online critical clearing time estimation

where integrated extended equal area criterion and extreme learning machine are employed as analytical model part and data-driven error correction model part

respectively. Derived results validate the credible efficacy of the proposed method.