电力系统及其自动化学报2017，Vol.29Issue(7)：87-92,6.DOI:10.3969/j.issn.1003-8930.2017.07.014
基于模态分析和Relief算法的在线静态电压稳定特征选取方法
Feature Selection Method for Online Static Voltage Stability Based on Modal Analysis and Relief Algorithm
摘要
Abstract
The selection and dimensionality-reduction of input features is one of the key issues related to the assessment on static voltage stability based on decision tree. Considering that the efficiency and accuracy of assessment results largely depends on the rationality of selected feature set,a feature selection method which can effectively reduce the number of dimensionalities is proposed.Firstly,a preliminary screening is done by using modal analysis to extract the key factors leading to voltage instability.Secondly,the extracted factors are further optimized by using Relief feature se?lection algorithm,thus a weight value is given for each feature variable,and those with lower weights are excluded. Fi?nally,a decision tree based 10-fold cross validation method is used to verify the assessment results of an actual power network. The results show that by using the feature selection method for static voltage stability,the accuracy of the ob?tained simplest combination in the prediction of static voltage stability is higher,and the modeling time is shorter .关键词
电压稳定/模态分析/数据挖掘/特征选取/决策树Key words
voltage stability/modal analysis/data mining/feature selection/decision tree分类
信息技术与安全科学引用本文复制引用
和怡,朱小军,李登峰,陈涛,张大海,张沛..基于模态分析和Relief算法的在线静态电压稳定特征选取方法[J].电力系统及其自动化学报,2017,29(7):87-92,6.基金项目
中央高校基本科研业务费专项基金资助项目(2015YJS158) （2015YJS158）
