中国电机工程学报2011，Vol.31Issue(1)：46-51,6.
基于受扰严重机组特征及机器学习方法的电力系统暂态稳定评估
Power System Transient Stability Assessment Based on Severely Disturbed Generator Attributes and Machine Learning Method
摘要
Abstract
It had been proved that the dynamic of severely disturbed machines can effectively be used to assess transient stability of bulk power system by theory and simulation research. A combined method was proposed to detect severely disturbed machines and construct original features based on critical machines. Furthermore, the dimensions of the features were reduced by principal component analysis. Then the abstract features were put into machine learning assessment model. In New England 39-bus test system and IEEE 50-generator test system, power system transient stability assessment models were simulated based on decision tree,support vector machine and k nearest neighbor classifier. The simulation results demonstrate the proposed approach's effectiveness to construct input features of power system transient stability assessment model based on machine learning method, and the approach helps to reduce the subjective and arbitrary constmction of original features.关键词
暂态稳定评估/机器学习/支持向量机/随机森林/主成分分析法Key words
transient stability assessment (TSA)/ machine learning method/ support vector machine (SVM)/ random forest/ principal component analysis (PCA)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..基于受扰严重机组特征及机器学习方法的电力系统暂态稳定评估[J].中国电机工程学报,2011,31(1):46-51,6.基金项目
国家自然科学基金项目(90610026) （90610026）
新世纪优秀人才支持计划项目(NECT-08-0825). （NECT-08-0825）
