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Transient Stability Analysis with Physics-Informed Neural Networks

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arxiv 2106.13638 v3 pith:VQ4T2Q6N submitted 2021-06-25 cs.LG cs.SYeess.SY

classification cs.LGcs.SYeess.SY
keywords neuralnetworksphysics-informedstabilitysystemtrainingtransientequations
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We explore the possibility to use physics-informed neural networks to drastically accelerate the solution of ordinary differential-algebraic equations that govern the power system dynamics. When it comes to transient stability assessment, the traditionally applied methods either carry a significant computational burden, require model simplifications, or use overly conservative surrogate models. Conventional neural networks can circumvent these limitations but are faced with high demand of high-quality training datasets, while they ignore the underlying governing equations. Physics-informed neural networks are different: they incorporate the power system differential algebraic equations directly into the neural network training and drastically reduce the need for training data. This paper takes a deep dive into the performance of physics-informed neural networks for power system transient stability assessment. Introducing a new neural network training procedure to facilitate a thorough comparison, we explore how physics-informed neural networks compare with conventional differential-algebraic solvers and classical neural networks in terms of computation time, requirements in data, and prediction accuracy. We illustrate the findings on the Kundur two-area system, and assess the opportunities and challenges of physics-informed neural networks to serve as a transient stability analysis tool, highlighting possible pathways to further develop this method.

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  1. Toolbox for Developing Physics Informed Neural Networks for Power Systems Components

    eess.SY 2025-02 conditional novelty 5.0 of 10

    A new open-source toolbox trains physics-informed neural networks for power system components and demonstrates a 9th-order synchronous machine with AVR and governor, reaching 2.26e-3 mean absolute error with fast inference.

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