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When performing remote sensing image segmentation, practitioners often encounter various challenges, such as a strong imbalance in the foreground–background, the presence of tiny objects, high object density, intra-class heterogeneity, and inter-class homogeneity. To overcome these challenges, this paper introduces AerialFormer, a hybrid model that strategically combines the strengths of Transformers and Convolutional Neural Networks (CNNs). AerialFormer features a CNN Stem module integrated to preserve low-level and high-resolution features, enhancing the model’s capability to process details of aerial imagery. The proposed AerialFormer is designed with a hierarchical structure, in which a Transformer encoder generates multi-scale features and a multi-dilated CNN (MDC) decoder aggregates the information from the multi-scale inputs. As a result, information is taken into account in both local and global contexts, so that powerful representations and high-resolution segmentation can be achieved. The proposed AerialFormer was benchmarked on three benchmark datasets, including iSAID, LoveDA, and Potsdam. Comprehensive experiments and extensive ablation studies show that the proposed AerialFormer remarkably outperforms state-of-the-art methods. 

Security is a well-known function to any transmission operator and system planner. As the world is moving toward the decarbonization of the power industry, it is more complicated for the system operators to maintain an acceptable level of security in the power system operation. More large-scale wind farms are being incorporated into the grid, and thus, the voltage stability concern is increasing. In practice, several contingencies are imagined by the system operators to assess the reliability of the grid. Since voltage stability is one of the major menaces that can trigger voltage instability in a power system, this paper is attempting to present to the transmission system planners and operators a dedicated methodology to facilitate the incorporation of large-scale wind farms into a transmission grid under high penetration of wind power. the stability of a wind-dominated power system is discussed based on Q-V and P-V methodologies and some N-1 contingencies with the Remedial Action Schemes (RAS). Furthermore, a methodology to rank the worst contingencies and to predict the voltage collapse during the highest wind penetration level is presented. Simulations have been, extensively, carried out to examine the methodology and have provided valuable information about the static security of the wind-dominated power system. The results can be used by the transmission system operator to anticipate voltage instability or voltage collapse in the power system during high wind penetration levels. 

The increasing capacity from inverter-based resources (IBR) creates challenges for designing and operating electric power systems. In particular, wind and solar generation has very different characteristics compared to conventional turbo generators. This research investigates the critical clearing times for IBR as larger amounts of wind generation brought online. This paper develops a new six-bus transmission test systems for which multiple wind stations are interconnected. An exhaustive study of fault locations with respect to load levels and line impedances for a wide range of IBR penetration levels was performed with respect to inverter stability analysis to determine the corresponding critical clearing times. The results show that voltage stability at IBR points of interconnection can occur at not only higher penetration levels, but at lower penetrations as well.