Cascading Failure Analysis Based on a Physics-Informed Graph Neural Network

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/fine-tuning 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
$\zeta$
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
$\zeta$
is properly pre-set.