全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于改进最大相关最小冗余判据的暂态稳定评估特选择

, PP. 179-185

Keywords: 暂态稳定评估,特征选择,最大相关最小冗余,支持向量机,相量测量单元

Full-Text   Cite this paper   Add to My Lib

Abstract:

提出一种基于改进最大相关最小冗余判据(maximalrelevanceandminimalredundancy,mRMR)的暂态稳定评估特征选择方法。首先对标准mRMR方法进行改进,在最大相关、最小冗余判据中引入一个权重因子以细化对特征相关性和冗余性的度量。然后,考虑相量测量单元可以提供的故障后实测信息,构造由系统特征构成的原始特征集,将改进的mRMR应用于特征选择。通过增量搜索算法得到一组嵌套的候选特征子集,并使用支持向量机分类器验证各候选特征子集的分类性能,选择得到具有最大分类正确率的特征子集。基于新英格兰39节点系统和IEEE50机测试系统的算例结果验证了所提特征选择方法的有效性。

References

[1]  Jensen C A,El-Sharkawi M A,Marks R J.Power system security assessment using neural networks:feature selection using Fisher discrimination[J].IEEE Transactions on Power Systems,2001,16(4):757-763.
[2]  Tso S K,Gu X P.Feature selection by separability assessment of input spaces for transient stability classification based on neural networks[J].International Journal of Electrical Power Energy Systems,2004,26(3):153-162.
[3]  叶圣永,王晓茹,刘志刚,等.基于支持向量机的暂态稳定评估双阶段特征选择[J].中国电机工程学报,2010,30(31):28-34.
[4]  Terzija V,Valverds G,Cai Deyu,et al.Wide-area monitoring,protection,and control of future electric power networks[J].Proceedings of the IEEE,2011,99(1):80-93.
[5]  卢芳,于继来.基于广域相量测量的暂态稳定快速评估方法[J].电力系统自动化,2010,34(8):24-28.
[6]  Peng Hanchuan,Long Fuhui,Ding Chris.Feature selection based on mutual information criteria of max- dependency,max-relevance and min-redundancy[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(8):1226-1238.
[7]  Vapnik V.The nature of statistical learning theory [M].New York:Springer Verlag,2000:138-170.
[8]  Reunanen J.Overfitting in making comparisons between variable selection methods[J].Journal of Machine Learning Research,2003(3):1371-1382.
[9]  Anderson P M,Fouad A A.Power system control and stability[M].2nd Edition.Piscataway,NJ:IEEE,2003:4-12.
[10]  顾雪平,张文朝.基于Tabu搜索技术的暂态稳定分类神经网络的输入特征选择[J].中国电机工程学报,2002,22(7):66-70.
[11]  刘艳,顾雪平,李军.用于暂态稳定评估的人工神经网络输入特征离散化方法[J].中国电机工程学报,2005,25(15):56-61.
[12]  Amjady N,Banihashemi S A.Transient stability prediction of power systems by a new synchronism status index and hybrid classifier[J].IET Generation, Transmission Distribution,2010,4(4):509-518.
[13]  叶圣永,王晓茹,刘志刚,等.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.
[14]  Gomez F R,Rajapakse A D,Annakkage U D,et al.Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements[J].IEEE Transactions on Power Systems,2011,26(3):1474-1483.
[15]  Jain A k,Duin R P W,Mao J C.Statistical pattern recognition:a review[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(1):4-37.
[16]  Guyon I,Elisseeff A.An introduction to variable and feature selection[J].Journal of Machine Learning Research,2003(3):1157-1182.
[17]  王皓,孙宏斌,张伯明,等.基于混合互信息的特征选择方法及其在静态电压稳定评估中的应用[J].中国电机工程学报,2006,26(7):77-81.
[18]  Vittal V.Transient stability test systems for direct stability methods[J].IEEE Transactions on Power Systems,1992,7(1):37-43.
[19]  Xu Y,Dong Z Y,Zhao J H,et al.A reliable intelligent system for real-time dynamic security assessment of power systems[J].IEEE Transactions on Power Systems,2012,27(3):1235-1263.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133