摘要
采用二进粒子群优化算法进行暂态稳定评估的特征选择,粒子群中每个粒子代表一个待选择的特征集,结合最小二乘支持向量机使用该特征集对所对应的样本集进行分类,分类正确率作为该粒子的适应度。首先通过二进粒子群优化实现特征的选择,然后将优选后的特征作为暂态稳定评估的输入,利用最小二乘支持向量机构造分类器进行暂态稳定评估。通过对EPRI-36节点系统的仿真计算,结果表明该方法能够在显著减少输入特征维数的同时大大提高最终判别结果的正确率。
This paper presents a method of feature selection for transient stability assessment based on binary particle swarm optimization（BPSO）, Every particle in the swarm stands for a selected subset of features. The fitness of particle is defined as the correct classification percentage by least square support vector machine （LS-SVM）, which uses the selected subset of features to classify the corresponding trainingset. First, this paper uses the BPSO to complete the feature selection, then inputs the selected features into LS-SVM classifier for transient stability assessment. It is tested on the EPRI-36 bus model of PSASP, the result indicates that the method can evidently decrease the dimensions of input features while greatly increase the correct classification percentage.
出处
《继电器》
CSCD
北大核心
2007年第1期31-36,50,共7页
Relay
基金
自然科学基金重大项目(50595412)
自然科学基金项目(50377017)~~
关键词
电力系统
暂态稳定评估
支持向量机
粒子群优化
特征选择
power system
transient stability assessment
support vector machine
particle swarm optimization
feature selection
