Time Series Data-Driven Batch Assessment of Power System Short-Term Voltage Security

Abstract

For power system dynamic security assessment (DSA), the conventional dynamic security region method is able to provide valuable information on security margins for preventive control. However, its event-based nature is likely to induce heavy computational burdens, especially in the presence of substantial presumed events. To tackle this challenging problem, this article develops an efficient time series data-driven scheme for batch DSA in a divide-and-conquer manner. First of all, with emphasis on short-term voltage stability, a novel u-shapelet (representative local trajectory)-based hierarchical clustering method is proposed to automatically divide various training cases into a handful of typical transient scenarios. Then, regressive shapelet learning is efficiently carried out to conquer individual scenarios, resulting in a group of high-precision security margin estimation models. With a desirable data-driven nature, the proposed scheme avoids time-consuming dynamic security region (DSR) characterization for each event, thereby achieving a significant speed-up for batch DSA. Test results on the realistic China Southern Power Grid illustrate its excellent performances on batch DSA.