基于扩展k阶近邻法的电力系统稳定评估新算法  

A New Method for Power System Stability Assessment Based on Extended k-Nearest Neighbor Classifier

作　　者：王同文[1] 管霖[1] 章小强[1] 张尧[1] 

机构地区：[1]华南理工大学电力学院,广东省广州市510640

出　　处：《电力系统自动化》2008年第3期18-21,75,共5页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(50407014)~~

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。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.

关 键 词：稳定评估 扩展k阶近邻法 模式发现 特征选择 知识获取 

分 类 号：TM712[电气工程—电力系统及自动化]