Static and vibration analysis of isotropic and functionally graded sandwich plates using an edge-based MITC3 finite elements

Static and vibration analysis of isotropic and functionally graded sandwich plates using a higher-order shear deformation theory is presented in this paper. Lagrangian functional is used to derive the equations of motion. The mixed interpolation of tensorial components (MITC) approach and edge-based-strain technique is used to solve problems. A MITC3 three-node triangle element with 7 degree-of-freedoms per nodes that only requires the C0-type continuity is developed. Numerical results for isotropic and functionally graded sandwich plates with different boundary conditions are proposed to validate the developed theory and to investigate effects of material distribution, side-to-thickness ratio, thickness ratio of layers and boundary conditions on the deflection, stresses and natural frequencies of the plates

1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems

This paper presents a 1-D convolutional graph neural network for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined recurrent graph convolutional neural networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The achievable accuracy of 99.27%, 98.1%, 98.75%, and 95.6% for fault detection, fault type classification, fault phase identification, and fault location respectively.Comment: arXiv admin note: text overlap with arXiv:2210.1517