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Data-driven small-signal assessment (SSA) is promising for long-term power system operation security. Existing SSA schemes are sensitive to unseen topology changes, especially when aimed at inter-area modes. We propose a Multi-hop Edge-Graph based Deep Learning (MEGDL) model for the SSA based on steady-state measurements. The core MEGDL includes multiple blocks. Each block utilizes not only the edge-graph convolution to jointly merge electrical variables at buses (nodes) and transmission lines (edges), but also exploits the multi-hop graph attention mechanism to discover the impact of topology changes on the high-order neighborhoods. Then the frequencies and damping ratios of various modes can be predicted in parallel via a multi-task structure with sharing features and different downstream networks. Test results on the IEEE 39 Bus system and the IEEE 118 Bus system indicate the superiority of our scheme over existing models, as well as its robustness against significant topology changes. • A small-signal assessment scheme with superior adaptivity to significant topology changes. • An edge-graph convolution extracts both node and edge features. • A multi-hop graph attention enhances model generalization under various topology changes. • Effective graph learning strategies and the multi-task scheme promotes the performance in critical LFO modes. [ABSTRACT FROM AUTHOR]

In the current work, the effects of initial deformation temperature and strain rate on flow behavior and microstructure of boron-alloyed steel after non-isothermal deformation were investigated. Based on the stress-strain curve obtained in the non-isothermal tensile experiment with a Gleeble 3500 system, finite element software was used to carry out the thermal mechanical coupled simulation of hot stamping process, which was further validated by hot stamping experiment of a V-channel part. The results indicate that the forming force was higher than that of the isothermal deformation. It was also observed that the initial stamping temperature should be above 750 °C, and the holding and quenching time should be up to 15 s in hot stamping process according to finite element (FE) simulation. Finally, the results obtained from hot stamping experiments are in good consistency with the simulation; thus, it reveals that the FE modeling of non-isothermal hot stamping process is reliable. [ABSTRACT FROM AUTHOR]

Data-driven transient stability assessment (TSA) models are usually sensitive to system scale changes and require dynamic information from time-domain simulation (TDS) as inputs. We propose a System-scAle-FreE Transient Contingency Screening (SAFE-TCS) scheme based on only the steady-state measurements. An analytical model is set up to estimate the state variation at fault occurrence ($t_{0+}$) snapshot, which forms multi-graph inputs together with the steady-state information. A novel pooling-ensemble multi-attention graph convolutional network (PE-MAGCN) realizes the spatio-temporal graph embedding, in which an inter-graph convolution link works for the temporal abstraction. Following a pooling-ensemble mechanism, PE-MAGCN derives a fixed-size expressive vector for task-specific networks. This promotes the robustness of the model against system extension. The advantages of SAFE-TCS also benefit from the coordination of various training tricks, including channel attention, category-balanced sampling and joint-decoupling learning, etc. Test results on IEEE 39 Bus system and IEEE 300 Bus system indicate the superiority of the proposed scheme over existing models and its adaptability under various scenarios. [ABSTRACT FROM AUTHOR]

• The model requires only pre-fault steady state information and the impact data at the fault occurrence period. • The method can provide multivariate STVS indices of each bus, and identify the instability patterns. • The heterogeneous graph attention network HGAT combine the zoning characteristics with the influence of bus type. • The improved multi-tasking learning (MTL) algorithm is proposed. • Excellent accuracy and robustness to topological changes. Compared with time-domain simulation (TDS), data-driven models show great advantage in time consumption of the power system security analysis. This paper proposes a novel graph neural network (GNN) based model for the short-term voltage stability (STVS) assessment. Based on mainly the steady-state information as the inputs, the model can provide multivariate stability indices for each bus and adapt to minor topological changes. Both the fast voltage collapses (FVC) and fault-induced delayed voltage recovery (FIDVR) events can be identified. A modified multi-task learning (MTL) process improves the multi-index evaluation performance of the model. As the kernel of the model, a heterogeneous graph attention deep network (HGAT) with various types of nodes works to aggregate the local information and generate new features. Then several micro-detectors scan over the network to provide the STVS indices of each bus with only the feature vector of one node as input for the prediction. Comparisons on the test system show that the proposed method can provide fine-grained STVS evaluation and show good robustness in the scenarios of different dynamic load distribution and network topology changes. [ABSTRACT FROM AUTHOR]

• A scheme develops TSA almost immediately after fault occurrence. • A physical-enhanced graph attention mechanism with residual structure for topology learning. • A piece-wise stability index (PSI) is proposed. • Both the stability category and stability level are predicted. • Excellent accuracy and robustness to noise and topological changes. Reliable and fast transient stability assessment (TSA) is significantly required for the power system emergency control. We propose a topology adaptive high-speed transient stability assessment (HSTSA) scheme, where the inputs of the model adopt only the pre-fault state and the dynamics at the fault occurrence snapshot. A novel multi-graph attention network with residual structure (ResGAT) is designed to capture the stability characteristics. ResGAT applies improved graph attention mechanism to enhance its adaptability to the power system topology changes and the residual structure helps to avoid network degeneration. Meanwhile, a new piece-wise transient stability index (PSI) is proposed for the stability level prediction. Integration of both the stability category and the stability level results increase the precision of the HSTSA scheme. Test results on IEEE 39 Bus system and IEEE 300 Bus system indicate the superiority of the proposed scheme over existing models and its robustness under various scenarios.. [ABSTRACT FROM AUTHOR]