中文摘要：

针对k阶近邻法分类时对样本的潜在结构信息未加利用这一缺陷，扩展k阶近邻法采用模式发现算法获取样本的空间分布知识，以获得的知识取代原始样本实现未知样本的分类。算法有效剔除了不利于分类的干扰样本，提高了分类精度和速度。在基于稳态运行信息的暂态稳定评估算法中，应用扩展k阶近邻法，实现了各种方式下稳定水平的正确判别。仿真结果验证了算法的有效性。算法作为一种通用的知识获取工具有广泛的应用前景。

英文摘要：

In view of the shortcoming of k-nearest neighbor （kNN） classification for neglecting sample structure information, a pattern discovery algorithm is adopted using the proposed extended k-nearest neighbor （EkNN） algorithm to obtain the spatial distribution knowledge from sample space which is then utilized to classify the unknown sample instead of the training set. The technique has eliminated the interfering samples unfavorable to classification while improving its accuracy and speed. The stability assessment scheme employs EkNN to identify the stability levels of input operation states based on pre-contingency steady state parameters. The simulation results of two IEEE test systems show the effectiveness of the proposed method. The EkNN algorithm as a knowledge acquisition tool can be applied to a wide range of engineering domains.