期刊
IEEE SYSTEMS JOURNAL
卷 17, 期 1, 页码 479-490出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2022.3150237
关键词
Mathematical models; Data models; Power system dynamics; Computational modeling; Predictive models; Extraterrestrial measurements; Transient analysis; Compressed sensing; deep learning; dynamic model; Koopman modes; neural network; nonlinear dynamics; power system; system identification
This article explores the analysis of transient phenomena in large-scale power systems subjected to major disturbances from the aspect of interleaving, coordinating, and refining physics- and data-driven models. The study proposes a framework that enables coordinated and seamlessly integrated use of the two types of models in engineered systems.
The article explores the analysis of transient phenomena in large-scale power systems subjected to major disturbances from the aspect of interleaving, coordinating, and refining physics- and data-driven models. Major disturbances can lead to cascading failures and ultimately to the partial power system blackout. Our primary interest is in a framework that would enable coordinated and seamlessly integrated use of the two types of models in engineered systems. Parts of this framework include: 1) optimized compressed sensing, 2) customized finite-dimensional approximations of the Koopman operator, and 3) gray-box integration of physics-driven (equation-based) and data-driven (deep neural network-based) models. The proposed three-stage procedure is applied to the transient stability analysis on the multimachine benchmark example of a 441-bus real-world test system, where the results are shown for a synchronous generator with local measurements in the connection point.
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