Transient Stability Analysis with Physics-Informed Neural Networks
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Solving the ordinary differential equations that govern the power system is an indispensable part in transient stability analysis. However, the traditionally applied methods either carry a significant computational burden, require model simplifications, or use overly conservative surrogate models. Neural networks can circumvent these limitations but are faced with high demands on the used datasets. Furthermore, they are agnostic to the underlying governing equations. Physics-informed neural network tackle this problem and we explore their advantages and challenges in this paper. We illustrate the findings on the Kundur two-area system and highlight possible pathways forward in developing this method further.
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