Robust fault recovery strategy for multi-source flexibly interconnected distribution networks in extreme disaster scenarios

To enhance the resilience of power distribution networks against extreme natural disasters, this article introduces a robust fault recovery strategy for multi-source, flexible interconnected power distribution networks, particularly under scenarios of extreme disasters. Initially, the comprehensive risk of system failure due to ice load on distribution lines and poles is fully considered, and a model for the overall failure rate of lines is constructed. This model addresses the diverse failure scenarios triggered by various meteorological conditions. Through the use of information entropy, typical extreme disaster failure scenarios are identified, and lines with high failure rates under these scenarios are determined. Subsequently, a box-type interval model is developed to represent the uncertainty in the output of distributed generation (DG), and on this basis, a robust fault recovery model for multi-source power distribution networks interconnected through soft open points (SOPs) is established, and use the Column and Constraint Generation (C&CG) algorithm to solve the problem. Finally, the fault recovery model and strategy proposed are validated through an illustrative example based on a modified IEEE 33-node interconnected system

Vapor-assisted synthesis of Al2O3-coated LiCoO2 for high-voltage lithium ion batteries

A simple and facile vapor-assisted hydrolysis route has been used to synthesize Al2O3-coated LiCoO2. The effects of Al2O3 coating on the structure and electrochemical performance have been systematically studied. After deposition of the Al2O3 coating, the crystal structure and morphology of LiCoO2 are maintained. Galvanostatic charge-discharge tests show that Al2O3-coated LiCoO2 exhibits markedly improved capacity retention and rate capability at high charge voltage (4.5 V). The Al2O3-coated LiCoO2 exhibits an initial discharge capacity of 165.5 mAh/g at 360 mA/g, and the capacity retention is 98.6% after 180 cycles. The electrochemical impedance spectroscopy results demonstrate that the Al2O3 coating can slow the increase of charge-transfer resistance during cycling, which is in accord with the excellent electrochemical cycling performance of Al2O3-coated LiCoO2

Further Results on Generalized Bent Functions and Their Complete Characterization

International audienc

Niobium doped anatase TiO2 as an effective anode material for sodium-ion batteries

Sodium-ion batteries are considered to be a promising low-cost alternative to common lithium-ion batteries in the areas where specific energy is less critical. Among all the anode materials studied so far, TiO2 is very promising due to its low operating voltage, high capacity, nontoxicity, and low production cost. Herein, we present Nb-doped anatase TiO2 nanoparticles with high capacity, excellent cycling performance, and excellent rate capability. The optimized Nb-doped TiO2 anode delivers high reversible capacities of 177 mA h g-1 at 0.1C and 108.8 mA h g-1 at 5C, in contrast to 150.4 mA h g-1 at 0.1C and only 54.6 mA h g-1 at 5C for the pristine TiO2. The good performance is likely to be associated with enhanced conductivity and lattice expansion due to Nb doping. These results, in combination with its environmental friendliness and cost efficiency, render Nb-doped TiO2 a promising anode material for high-power sodium-ion batteries

DGSVis: Visual Analysis of Hierarchical Snapshots in Dynamic Graph

Dynamic graph visualization attracts researchers' concentration as it represents time-varying relationships between entities in multiple domains (e.g., social media analysis, academic cooperation analysis, team sports analysis). Integrating visual analytic methods is consequential in presenting, comparing, and reviewing dynamic graphs. Even though dynamic graph visualization is developed for many years, how to effectively visualize large-scale and time-intensive dynamic graph data with subtle changes is still challenging for researchers. To provide an effective analysis method for this type of dynamic graph data, we propose a snapshot generation algorithm involving Human-In-Loop to help users divide the dynamic graphs into multi-granularity and hierarchical snapshots for further analysis. In addition, we design a visual analysis prototype system (DGSVis) to assist users in accessing the dynamic graph insights effectively. DGSVis integrates a graphical operation interface to help users generate snapshots visually and interactively. It is equipped with the overview and details for visualizing hierarchical snapshots of the dynamic graph data. To illustrate the usability and efficiency of our proposed methods for this type of dynamic graph data, we introduce two case studies based on basketball player networks in a competition. In addition, we conduct an evaluation and receive exciting feedback from experienced visualization experts.Comment: 11 pages, 9 figure

Research on the remaining life prediction technology of environmentally friendly gas switchgear based on approximate dynamic programming

The transformation of energy structure has brought about new changes in the power system, and the environmentally friendly gas switchgear with the goal of low carbon and environmental protection has been widely spread and applied, but due to its short application time and the accumulation of related research, it continues to carry out life prediction to improve the related operation and maintenance system. The article proposes the environmental protection gas switchgear data processing technology based on data enhancement technology, proposes the environmental protection gas switchgear life prediction technology based on approximate dynamic planning, and verifies the algorithm through case analysis, which proves the effectiveness and accuracy of the proposed method