The identification of vulnerable lines in smart grid systems is of great significance to increase the stability of the smart grid systems and reduce the occurrence of cascading fault blackouts. Inspired by the machine learning method, this study proposes a vulnerable line identification approach based on the improved agglomerative hierarchical clustering algorithm. By jointly considering the topological parameters and the electrical properties, we discuss the vulnerability of the transmission lines and establish the influencing factors. Then, we adopt principal component analysis (PCA) to select the influencing factors and reduce their dimensionality. Finally, an improved agglomerative hierarchical clustering algorithm is proposed and employed to divide the lines to identify the vulnerable lines in the smart grid systems. Experiments over the IEEE 39-bus system demonstrate that our proposed method can efficiently and accurately identify different types of potential vulnerable lines in smart grid systems.

Guangxi is a typical developing region on the southern coast of China. The current issues encountered in the region’s development are that fossil energy accounts for about 80% of the energy structure, fossil fuels are heavily dependent on imports, and the self-sufficiency rate of resources is only 32%. These challenges have created a disparity between the current regional development state and the country’s dual carbon target. Under the premise of comprehensively considering the multi-sectors of electricity, industry, transportation, and heating, this paper presents a study on the energy system transition towards low-carbon development for Guangxi in four steps. Firstly, to demonstrate EnergyPLAN’s capability in energy modeling, a reference scenario for Guangxi is created using official yearbook data from 2020. Then, a short-term scenario is formulated to analyze the development of Guangxi’s energy system during the 14th Five-Year Plan. Furthermore, two mid-term scenarios are established, revealing that Guangxi is anticipated to reach its carbon emission peak between 2025 and 2030. Finally, three long-term scenarios are proposed for Guangxi’s energy system for 2050. These scenarios encompass the expansion of photovoltaics, nuclear, and wind power in the electricity system and emission reduction policies in the industrial, transportation, and heating sectors. As a result, compared with the 2020REF scenario, Guangxi can achieve a carbon emission reduction exceeding 57% and the share of non-fossil energy consumption can reach about 70% in the 2050 scenarios, despite a substantial increase in energy consumption, which makes it possible to achieve carbon neutrality in 2060 and to establish an energy system with less than 20% of fossil energy consumption.

In this letter, a simplified single current loop control scheme for single-phase dual-boost inverter has been developed, combining half cycle modulation and virtual-vector based model predictive control (MPC). As the control reference can be slipped and allocated into two side boost, only a unified grid current reference is relied on, in the proposed control method. Furthermorewith the concept of feedforward control, the global optimal solution space in MPC can be compressed, taking the duty ratio as boundary for the pre-compacted solution space. Thereby, the improved MPC can select the optimal virtual-vector in a smaller and closer subspace. Consequently, much finer control effect can be obtained. Experimental tests are provided to validate the proposed solution and its merits.

The underlying mechanisms of eating disorders (EDs) are very complicated and still poorly understood. The pathogenesis of EDs may involve the interplay of multiple genes. To investigate the dysregulated gene pathways in EDs we analyzed gene expression profiling in dorsolateral prefrontal cortex (DLPFC) tissues from 15 EDs cases, including 3 with anorexia nervosa (AN), 7 with bulimia nervosa (BN), 2 AN-BN cases, 3 cases of EDs not otherwise specified, and 102 controls. We further used a weighted gene co-expression network analysis to construct a gene co-expression network and to detect functional modules of highly correlated genes. The functional enrichment analysis of genes in co-expression modules indicated that an altered mitochondrial oxidative phosphorylation process may be involved in the pathogenesis of EDs.

The integrity and reliability of wind turbine electrical data (such as active power, voltage, current, etc.) are crucial for operational monitoring, fault diagnosis, and predictive analysis in wind energy systems. However, due to various reasons such as hardware failures, network communication issues, environmental interference, and human errors, data gaps still exist in the Supervisory Control and Data Acquisition (SCADA) systems. Existing multivariate wind power time series imputation methods face two main limitations: (1) inadequate handling of continuous missing patterns (band missing and feature missing) and (2) insufficient utilization of spatiotemporal and feature correlations among wind turbines. To address these shortcomings, this study proposes an imputation framework that includes two types of SCADA data missing scenarios in wind turbines. For band missing, the framework leverages similar wind turbine data matching to explore spatiotemporal correlations in wind power data. For feature missing, the framework focuses on feature correlations in wind power data using Pearson coefficients and normalized mutual information. Additionally, we designed a novel Dual-Type Deep Convolutional Generative Adversarial Imputation Network (DT-DCGAIN) model within this framework to impute different types of missing data. Finally, by evaluating the proposed method on real-world wind farm SCADA datasets, it achieved a 13.91% to 28.32% improvement in Root Mean Square Error (RMSE). Ablation experiments on the model further validated the contributions of each correlation extraction module.

Traffic accidents frequently occur at the nose of highway exit ramps for a variety of reasons. When attempting to take an exit, a driver’s line of sight may be obscured by the curvature or slope of the main expressway, roadside vegetation, traffic signs, and other vehicles traveling alongside. These increase the likelihood of accidents due to inaccurate or poor driver judgement when approaching a highway exit. Because of emotional responses that accompany the possibility of missing an exit, many drivers act irrationally through haphazard acceleration or braking, aggressive lane changes; in some cases, even coming to a complete stop and reversing against the flow of high speed traffic to correct their course. This study applies to curved highway exit corridors with a poor line of sight. The total visible area of the exit ramp is defined in accordance with the design specifications of both the highway routes and exit ramps, which was then further refined by considering the driving characteristics of both the drivers and of surrounding traffic. This study set out to establish a reliable occlusion model of highway exits with the following goals in mind. Geometric equations describing the area and volume of the target area of the exit ramp are established. Discrete expression equation was deployed to establish the occlusion model for the target area within the visual field. Parameters taken from specifications of the Hunan section of the Shanghai-Kunming Expressway are used for multiple calculations, in which obstruction patterns of objects with the same width, and objects with different width at different positions are analyzed. Based on the patterns observed from stimulated experiments mentioned above, multiple effective measures were proposed to prevent the obstruction of the highway exit ramp. In summary, this study provides theoretical support for prevention of obstruction as well as an anti-occlusion model for better identification of the highway exit ramps by intelligent vehicle systems.