In the full process of extended equal area criterion(EEAC)algorithm

dynamic EEAC(DEEAC)is the bridge between analytical static EEAC(SEEAC)and accurate integrating EEAC(IEEAC). For better coordinating the accuracy of IEEAC and the speedability of SEEAC

the influence of the subsection number of DEEAC on the performance of full EEAC is studied in detail. Based on theoretical analysis as well as the machine learning method

an adaptive segmental Taylor series expansion and dimensional reduction mapping are adopted to replace the 2-section expansions for post-fault in DEEAC. The difference between the stability margin given by the original DEEAC and that given by SEEAC is defined as the time-varying index. According to the index

the number of subsections in DEEAC can be adjusted automatically. Its robust performance is verified by 36 sets of data from 9 actual Chinese regional and provincial power systems

where both symmetric and asymmetric faults are considered

totally 3 272 cases. Compared with the average computational burden of the original DEEAC

the computational burden of the proposed adaptive DEEAC increases about 4%

however

the analysis precision for strong time-varying cases

as well as the effectiveness of the classifier for stable cases are significantly improved.