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Interleaving physics- and data-driven models for power system transient dynamics
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DOI：10.1016/j.epsr.2020.106824.png)
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Abstract
En 中文
The paper explores interleaved and coordinated refinement of physicsand data-driven models in describing transient phenomena in large-scale power systems. We develop and study an integrated analytical and computational data-driven gray box environment needed to achieve this aim. Main ingredients include computational differential geometry-based model reduction, optimization-based compressed sensing, and a finite approximation of the Koopman operator. The proposed two-step procedure (the model reduction by differential geometric (information geometry) tools, and data refinement by the compressed sensing and Koopman theory based dynamics prediction) is illustrated on the multi-machine benchmark example of IEEE 14-bus system with renewable sources, where the results are shown for doubly-fed induction generator (DFIG) with local measurements in the connection point. The algorithm is directly applicable to identification of other dynamic components (for example, dynamic loads).
Keywords:
Power system dynamics
Modeling
Physics-based models
Data-driven models
Compressed sensing
Koopman modes
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