电力系统及其自动化学报2016，Vol.28Issue(12)：103-108,6.DOI:10.3969/j.issn.1003-8930.2016.12.017
极限学习机和遗传算法在暂态稳定评估特征选择中的应用
Application of Extreme Learning Machine and Genetic Algorithm to Feature Selection of Transient Stability Assessment
卢锦玲 1於慧敏1
作者信息
- 1. 华北电力大学电气与电子工程学院，保定 071003
- 折叠
摘要
Abstract
Feature selection and input dimension reduction are important for the transient stability assessment of power system. To solve the problems in the existing feature selection methods,such as low efficiency and unsatisfactory de⁃composing subset result,a method is proposed based on extreme learning machine(ELM)and genetic algorithm. First,genetic algorithm is used to realize feature selection. Then the selected feature is input into ELM classifier for transient stability assessment. There are two factors in constructing the fitness function:one is that the selected feature subset should have a greater contribution to the classification;the other is that the adopted input features should be as less as possible. The application to a 10-Machine 39-Bus New England power system indicates that the effect is obvious⁃ly better after feature selection. Compared with other methods in the literature,the classification accuracy of the pro⁃posed approach is higher,which demonstrates its validity and advantage.关键词
电力系统/暂态稳定评估/特征选择/遗传算法/极限学习机Key words
power system/transient stability assessment/feature selection/genetic algorithm/extreme learning ma-chine(ELM)分类
信息技术与安全科学引用本文复制引用
卢锦玲,於慧敏..极限学习机和遗传算法在暂态稳定评估特征选择中的应用[J].电力系统及其自动化学报,2016,28(12):103-108,6.
