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Cascading Failure Analysis Based on a Physics-Informed Graph Neural Network
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DOI：10.1109/TPWRS.2022.3205043.png)
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Abstract
En 中文
Power flow calculation in quasi-steady states is the basis of cascading failure analysis. However, in recent years, data driven analysis methods that are based on sufficient data have put forward higher requirements on the speed of power flow calculation. To build a more accurate and efficient neural network for power flow calculation, a physics-informed graph neural network based model is proposed for faster calculation. Via minimizing the physics-informed loss function and using a pre-training/finetuning method, the proposed model is trained to follow the physical equations directly and can generalize to dynamic power networks. Physics-informed LOSS makes the proposed model more interpretable, since the calculation error can be evaluated by LOSS. Then cascading failures are simulated with the proposed model, and a pre-set factor ? is introduced to balance the speed and accuracy of simulations. Finally, the accuracy of cascading failure simulations with the proposed model is verified in the IEEE 39-bus system, the 118-bus system, the 300-bus system, and a real-world French system. Experimental results show that compared with AC power flow, the proposed physics-informed graph neural network based power flow model can reduce the simulation time significantly while maintaining high accuracy if ? is properly pre-set.
Keywords:
Cascading failure
power flow
graph neural work
physics-informed
quasi-steady state
Journal
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