Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 16, Issue 12, Pages 7306-7317Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.2977456
Keywords
Security; Power system stability; Trajectory; Estimation; Power system dynamics; Time series analysis; Thermal stability; Dynamic security region; security margin; shapelets; short-term voltage stability; time series data analytics
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Funding
- National Key Research and Development Program of China [2018YFB0904500]
- National Natural Science Foundation of China [U1766214, 51677097, TII-19-2546]
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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.
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