| 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 |
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| 引用本文： | 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(1). |
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| 作者姓名： | 叶圣永 王晓茹 刘志刚 钱清泉 |
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| 作者单位： | 西南交通大学电气工程学院; |
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| 基金项目： | 国家自然科学基金项目(90610026); 新世纪优秀人才支持计划项目(NECT-08-0825)~~ |
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| 摘 要： | 理论和仿真研究表明,依靠少量受扰严重机组的动态特征能够有效地判别大电网的暂态稳定性。提出一种组合搜索严重受扰机组,并据此构造稳定评估原始输入特征的方法。进一步利用主成分分析法降低特征维数,构成机器学习评估模型的输入特征。在新英格兰39节点测试系统和IEEE 50机测试系统上,利用所提方法仿真实现了决策树、支持向量机和k最近邻法等暂态稳定评估模型,结果表明所提出的构建电力系统暂态稳定评估输入特征方法有效,有助于改变原始特征构建的主观和随意性。
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| 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 |
Power System Transient Stability Assessment Based on Severely Disturbed Generator Attributes and Machine Learning Method |
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| YE Shengyong,WANG Xiaoru,LIU Zhigang,QIAN Qingquan.Power System Transient Stability Assessment Based on Severely Disturbed Generator Attributes and Machine Learning Method[J].Proceedings of the CSEE,2011(1). |
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| Authors: | YE Shengyong WANG Xiaoru LIU Zhigang QIAN Qingquan |
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| Affiliation: | YE Shengyong,WANG Xiaoru,LIU Zhigang,QIAN Qingquan(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,Sichuan Province,China) |
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| 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... |
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| Keywords: | transient stability assessment(TSA) machine learning method support vector machine(SVM) random forest principal component analysis(PCA) |
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