关键输入特征选择与评估模型是基于人工智能（AI）的电力系统暂态稳定评估方法研究的关键点．文中采用数据驱动的特征选择和规则提取算法，通过仿真实例评估关键特征，并提取稳定判别规则．在特征选择中采用基于遗传算法的k阶近邻法（GA-knn）评价特征的性能；在规则提取中，采用关联规则的分类器构造算法，生成暂态稳定评估规则．通过对10机39节点系统和3机9节点系统中应用结果的对比分析，在53维候选特征中得出了相对通用的暂态稳定评估关键特征，并得到不同网架结构中稳定评判规则表现出的适用性和在稳定边界上的特异性．

This paper deals with two key issuses of the artificial intelligence （AI） -based transient stability assess- ment （TSA） of power system, namely the selection of kernel input features and the stability-related evaluation mo- del. In the investigation, first, data-driven feature selection method and rule extraction algorithm are proposed. Then, the key features are evaluated and the transient stability rules are made from the training samples. During the feature selection, a genetic algorithm-based k-nearest neighbor （GA-knn） is used to assess the input features. Du- ring the rule extraction, a mining algorithm of classification and association rules is followed to form the rules of transient stability assessment. The proposed method is then applied to both the New England 10-machine 39-bus and the 3-machine 9-bus systems, and the results are compared and analyzed. It is found out that the selected ker- nel features from 53 candidates and the obtained rules are adapted for the two test power systems. However in the stability boundary, evaluation rules are complex and specific.