The increasing deployment of multi-energy distribution systems (MDSs) necessitates the coordinated restoration of power and thermal loads under disasters. Nevertheless, existing research often overlooks the inherent uncertainties of disasters and the potential of market mechanisms, resulting in risks of over-investment and inefficient allocation of flexible resources. Thus, this paper presents a distributed market-aided restoration approach for MDS considering the comprehensive uncertainties from typhoon disasters. First, a stress intensity interference theory combined with a structural fragility model is utilized to characterize the uncertainty of distribution line damage caused by typhoons. Second, a tractable operational model for MDS, incorporating numerous auxiliary variables, is introduced to simplify the constraints of power and thermal networks. Then, a risk-averse two-stage restoration framework is developed to manage comprehensive uncertainties from both line damage and renewable generation. Meanwhile, a Cournot Nash-based joint bidding model of multi-energy microgrids is designed as a market-aided strategy to facilitate the restoration of MDS. Further, a distributed algorithm named alternating search procedure is utilized to protect participants privacy and alleviate the computation burdens. Finally, case studies based on real statistics of the 2024 super typhoon “Yagi” in China validate that the proposed method creates a win-win situation for all stakeholders.

On 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention. Rapid acquisition of landslide inventories, distribution patterns, and key controlling factors is critical for post-disaster emergency response and reconstruction. Based on high-resolution Planet satellite imagery, landslide areas in Jiangwan Town were automatically extracted using the Normalized Difference Vegetation Index (NDVI) differential method, and a detailed landslide inventory was compiled. Combined with terrain, rainfall, and geological environmental factors, the spatial distribution and causes of landslides were analyzed. Results indicate that the extreme rainfall induced 1426 landslides with a total area of 4.56 km², predominantly small-to-medium scale. Landslides exhibited pronounced clustering and linear distribution along river valleys in a NE–SW orientation. Spatial analysis revealed concentrations on slopes between 200–300 m elevation with gradients of 20–30°. Four machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to assess landslide susceptibility mapping (LSM) accuracy. RF and XGBoost demonstrated superior performance, identifying high-susceptibility zones primarily on valley-side slopes in Jiangwan Town. Shapley Additive Explanations (SHAP) value analysis quantified key drivers, highlighting elevation, rainfall intensity, profile curvature, and topographic wetness index as dominant controlling factors. This study provides an effective methodology and data support for rapid rainfall-induced landslide identification and deep learning-based susceptibility assessment.

Typhoon disasters may cause power outages in a large area of users. It is quite important to conduct power outage risk assessment in advance and make adequate preparations for emergency decision-making. Starting from the procedure of risk assessment under typhoon disasters: predicting wind speed, calculating wind-induced failure probability, generating fault set, and calculating failure consequences and risks, this paper summarized methods for wind field model, failure analysis methods, and the manifestations of failure consequences in risk assessment. Then we compared the pros and cons for these methods, respectively. The conclusion is that combining software to process higher-resolution wind speed data and considering random wind field, using data mining statistical models to compute and judge fault status and actively calculating pre-typhoon failure risk will become future trend.

With the continuous expansion of power grids and the advancement of ultra-high voltage (UHV) projects, transmission lines are increasingly traversing areas characterized by micro-terrain. These localized topographic features can intensify meteorological effects, thereby increasing the risks of hazards such as conductor icing and galloping, directly threatening operational stability. Enhancing the disaster resilience of transmission lines in such environments requires accurate and efficient terrain identification. However, conventional recognition methods often neglect the spatial alignment of the transmission lines, limiting their effectiveness. This paper proposes a deep learning-based recognition framework that incorporates a dual-branch network architecture and a cross-branch spatial attention mechanism to address this limitation. The model explicitly captures the spatial correlation between transmission lines and surrounding terrain by utilizing line alignment information to guide attention along the line corridor. A semi-synthetic dataset, comprising 6495 simulated samples and 130 real-world samples, was constructed to facilitate model training and evaluation. Experimental results show that the proposed model achieves classification accuracies of 94.6% on the validation set and 92.8% on real-world test cases, significantly outperforming conventional baseline methods. These findings demonstrate that explicitly modeling the spatial relationship between transmission lines and terrain features substantially improves recognition accuracy, offering important support for hazard prevention and resilience enhancement in UHV transmission systems.