Nature

Data-driven dynamic modeling for inverter-based resources using neural networks

Dynamic models are a cornerstone of power system stability and control. The growing penetration of inverter-based resources, driven by global decarbonization, significantly complicates power system ...
Nature

Physics-informed neural networks for robust equivalent damping parameter inversion and fault diagnosis in gas-insulated switchgear vibration systems

Accurate simulation of vibration signals is essential for fault detection in power equipment such as gas-insulated switchgear (GIS) and transformers. The Finite Element Method (FEM) is commonly ...
American Institute of Physics

Hybrid convolutional neural network yields energy-efficient approach with analog first layer

Oscillatory retinal neuron networks don’t require external voltage sources and show comparable performance to cutting-edge GPU-based convolutional neural networks, for energy costs thousands of times ...