电力系统保护与控制2011，Vol.39Issue(6)：12-16,23,6.
基于 Stacking元学习策略的电力系统暂态稳定评估
Power system transient stability assessment based on Stacking meta-learning strategy
摘要
Abstract
In order to increase singe model's accuracy in transient stability assessment of power systems, the transient stability assessment based on meta-learning strategy is studied, and a Stacking assessment model is presented. The base learning includes support vector machines, decision trees, naive Bayesian and K-nearest neighbor classifier. Linear regression is adopted as Stacking assessment model of meta-learning. The model uses the probabilistic output of base learning algorithm as input attributes in a new training set, and keeps the original class labels. The final transient stability assessment result is acquired after learning in the new training set by linear regression. The simulations on New England 39-bus and IEEE 50-machcine test systems show that the assessment performance of the proposed approach is better than that of the single models and provides a new way to assess power system transient stability.关键词
暂态稳定评估/朴素贝叶斯/支持向量机/决策树/K最近邻法/Stacking算法Key words
transient stability assessment/ naive Bayesian/ support vector machines/ decision trees/ K- nearest neighbor classifier/Stacking algorithm分类
信息技术与安全科学引用本文复制引用
叶圣永,王晓茹,刘志刚,钱清泉..基于 Stacking元学习策略的电力系统暂态稳定评估[J].电力系统保护与控制,2011,39(6):12-16,23,6.基金项目
国家自然科学基金项目(No.90610026) （No.90610026）
教育部新世纪优秀人才支持计划(NECT-08-0825) （NECT-08-0825）
中央高校基本科研业务费专项资金资助(SWJTU09ZT10) （SWJTU09ZT10）
教育部霍英东青年教师基金(101060) （101060）
