Accurate wind power forecasting is essential for the stability of modern power systems. However, current probabilistic forecasting frameworks often encounter a fundamental conflict between the computational efficiency required for high-dimensional meteorological pattern matching and the physical consistency of the resulting probability distributions. Existing methods frequently fail to maintain the logical monotonicity of quantiles or overlook the fine-grained temporal correlations in massive historical datasets. To address these critical gaps, this research develops a comprehensive framework that synergizes a hierarchical similarity filtering mechanism with a structurally constrained non-crossing quantile regression model. First, the target sample is partitioned into several weather segments, and a new two-stage high-similarity weather pattern matching method is developed to screen multiple sets of historical samples that are highly similar to the target weather pattern. Second, a deep learning model for probabilistic wind power quantile forecasting is proposed, which incorporates historical data augmentation. The model utilizes an attention mechanism to extract the correlation between the target and historical segments, while an improved non-crossing quantile regression model is adopted to ensure the validity of the output quantiles. Finally, the effectiveness of the proposed method is validated through case studies using real-world data from an actual wind farm.

Due to the influence of the control strategy of the battery energy storage station (BESS), the degree of voltage sag, and the battery state of charge (SOC), the fault current characteristics of the BESS outlet line differ significantly. Traditional overcurrent protection faces the risk of failure to operate. To evaluate the operational performance of overcurrent protection of outlet line connecting BESS, this work first analyzes the topological structure and control strategy of BESS and further investigates the fault current characteristics of its outlet line. Based on this, the operational performance of overcurrent protection relay is studied. In addition, an overcurrent protection method of outlet line connecting BESS considering the battery SOC is proposed. By calculating and setting the SOC boundary, reliable protection of outlet line within different SOC intervals is achieved. Finally, a grid-connected model of BESS is built based on an electromagnetic transient simulation software to verify the operational characteristics of the proposed method.