标题： 基于学习方法的暂态稳定评估比较分析 显示英文标题

标题： Comparative Analysis of Learning-Based Methods for Transient Stability Assessment

摘要： 暂态稳定性和临界清除时间（CCT）是电力系统保护和控制中的重要概念。 本文探讨并比较了在可再生能源发电、负荷和故障引起的不确定性下预测CCT的各种基于学习的方法。 特别地，我们从工程角度引入了暂态稳定性（B-稳定性）和CCT的新定义。 在训练模型时，仅使用系统变量的初始值和故障情况作为特征，从而可以根据这些初始值提供保护信息。 为了提高效率，采用结合最大信息系数（MIC）和斯皮尔曼相关系数（SCC）的混合特征选择策略来减少特征维度。 不同基于学习的模型的性能在WSCC 9节点系统上进行了评估。 显示英文摘要

摘要： Transient stability and critical clearing time (CCT) are important concepts in power system protection and control. This paper explores and compares various learning-based methods for predicting CCT under uncertainties arising from renewable generation, loads, and contingencies. Specially, we introduce new definitions of transient stability (B-stablilty) and CCT from an engineering perspective. For training the models, only the initial values of system variables and contingency cases are used as features, enabling the provision of protection information based on these initial values. To enhance efficiency, a hybrid feature selection strategy combining the maximal information coefficient (MIC) and Spearman's Correlation Coefficient (SCC) is employed to reduce the feature dimension. The performance of different learning-based models is evaluated on a WSCC 9-bus system.