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Scalable Neural Dynamic Equivalence for Power Systems by Shen, Qing; Zhou, Yifan; Zhao, Huanfeng; Zhang, Peng; Zhang, Qiang; Maslenniko, Slava; Luo, Xiaochuan is a scholarly article available to read on EtoBox.

What is Scalable Neural Dynamic Equivalence for Power Systems about?

Traditional grid analytics are model-based, relying strongly on accurate models of power systems, especially the dynamic models of generators, controllers, loads and other dynamic components. However, acquiring thorough power system models can be impractical in real operation due to inaccessible system parameters and privacy of consumers, which necessitate data-driven dynamic equivalencing of unknown subsystems. Learning reliable dynamic equivalent models for the external systems from SCADA and PMU data, however, is a long-standing intractable problem in power system analysis due to complicated nonlinearity and unforeseeable dynamic modes of power systems. This paper advances a practical application of neural dynamic equivalence (NeuDyE) called Driving Port NeuDyE (DP-NeuDyE), which exploits physics-informed machine learning and neural-ordinary-differential-equations (ODE-NET) to discover a dynamic equivalence of external power grids while preserving its dynamic behaviors after disturbances. The new contributions are threefold: A NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems, saving the effort and expense of acquiring inaccessible

Author

Shen, Qing; Zhou, Yifan; Zhao, Huanfeng; Zhang, Peng; Zhang, Qiang; Maslenniko, Slava; Luo, Xiaochuan

Published

2023

Language

EN