Integrated Data-Driven Power System Transient Stability Monitoring and Enhancement

While many promising data-driven power system transient stability assessment (TSA) studies have been recently reported, very few of them further propose efficient data-driven solutions for follow-up control actions, e.g., generator tripping, against potential instability. To address this inadequacy, this work develops an integrated data-driven transient stability monitoring and enhancement (TSMAE) approach that can reliably and efficiently handle various emergency situations in real time. First, by introducing the emerging spatial-temporal synchronous graph convolutional network (STSGCN), wide-area spatial-temporal features w.r.t. system stability are sufficiently learned to reliably implement online TSA. Then, to handle impending instability in a tractable manner, remedial actions are quickly taken based on intelligent critical generator identification (CGI). Specifically, with the help of the STSGCN again, the potential effects of tripping individual generators on system stabilization are efficiently predicted from the spatial-temporal perspective. Based upon that, the most critical generators for tripping are adaptively selected to enhance system stability. Numerical test results on a realistic provincial power grid of China illustrate the efficacy of the proposed TSMAE approach.