面向配电网故障数据的BIC评估后向选择方法

doi:10.13190/j.jbupt.2017.03.015

北京邮电大学学报 ›› 2017, Vol. 40 ›› Issue (3): 104-109.doi: 10.13190/j.jbupt.2017.03.015

面向配电网故障数据的BIC评估后向选择方法

曾兴东^1,2, 林荣恒^1,2, 邹华¹, 张勇³

1. 北京邮电大学网络与交换技术国家重点实验室, 北京 100876;
2. 中国电子科技集团公司第五十四研究所通信网信息传输与分发技术重点实验室, 石家庄 050081;
3. 国家电网上海电力公司, 上海 200122

收稿日期:2016-08-04 出版日期:2017-06-28 发布日期:2017-05-25
作者简介:曾兴东(1992-),男,硕士生,E-mail:zengxdbupt@163.com;邹华(1969-),女,教授,硕士生导师.
基金资助:
国家高技术研究发展计划（863计划）项目（2015AA050203）；北京市自然科学基金项目（4174099）

An BIC Selection Method for Distribution Network Fault Data Feature Dimension Reduction

ZENG Xing-dong^1,2, LIN Rong-heng^1,2, ZOU Hua¹, ZHANG Yong³

1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory, The 54 th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China;
3. State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China

Received:2016-08-04 Online:2017-06-28 Published:2017-05-25

摘要/Abstract

摘要： 10 kV配电网所处环境复杂，引发故障的原因很多，在使用数据挖掘方法对配电网故障进行分析时，太多的特征会对挖掘模型造成负面影响.为了防止挖掘模型考虑过多无用信息，需首先对数据进行特征选择来实现降维，因此提出了基于贝叶斯信息准则（BIC）的模型评估后向选择算法，对故障因素进行降维.BIC评估准则能够尽可能地简化模型，降低维度，而后向选择算法可以快速得到最优的简化模型，两者的结合提升了降维的速度，并能够得到更加简化的模型.实验结果表明，采用基于BIC评估的后向选择算法有助于后续模型准确性的提升，可提高训练效率.

关键词: 配电网故障分析, 降维, BIC模型评估, 后向选择算法

Abstract: Feature selection is important to improve the model accuracy and reduce overfitting. The 10 kV power distribution network is complex and there are too many features for a data mining model to work. Before modeling power fault data, the dimensionality reduction and model selection is necessary. In order to solve this problem, a Bayesian information criterions (BIC) model selection algorithm along with backward selection algorithm was proposed. BIC aims to reduce the complexity of model and the backward selection can reach fast convergence. Experiments show that the algorithm works well. It is proven that the algorithm proposed here is of advantage to improve model accuracy and data training efficiency.

Key words: distribution network fault data, dimensionality reduction, Bayesian information criterions, backward selection algorithm

中图分类号:

曾兴东, 林荣恒, 邹华, 张勇. 面向配电网故障数据的BIC评估后向选择方法[J]. 北京邮电大学学报, 2017, 40(3): 104-109.

ZENG Xing-dong, LIN Rong-heng, ZOU Hua, ZHANG Yong. An BIC Selection Method for Distribution Network Fault Data Feature Dimension Reduction[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2017, 40(3): 104-109.

参考文献

[1] 彭向阳, 詹清华, 周华敏. 广东电网同塔多回线路雷击跳闸影响因素及故障分析[J]. 电网技术, 2012, 36(3):81-87.
[2] 刘笑园, 岑梁, 王震宇, 等. 配电网故障分析及故障指示器的现场应用[J]. 上海电力学院学报, 2015, 31(B05):12-14.
[3] 张勇, 陈凤. 长沙地区配电运行事故统计分析[J]. 中国电力, 2007, 40(5):28-30.
[4] 钟意伟, 赵杰煜, 朱绍军. 基于贝叶斯和谐度的特征选择算法[J]. 计算机工程与应用, 2015, 51(23):125-130.
[5] Robnik M, Kononenko I. Theoretical and empirical analysis of ReliefF and Relief-F[J]. Machine Learning, 2003, 53(1/2):23-69.
[6] Elham C, Taheri M, Katebi S, et al. An improved fuzzy feature clustering and selection based on Chi-Squared-Test[J]. Proceedings of the International MultiConference of Engineers and Computer Scientists 2009. Hong Kong:IEEE, 2009:18-20.
[7] Kumar G. Mutual information based feature selection techniques for intrusion detection[J]. International Advanced Research Journal in Science, Engineering and Technology, 2014, 1(2):70-75.
[8] Qu Guangzhi, Hariri S, Yousif M. A new dependency and correlation analysis for features[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(9):1199-1207.
[9] Raftery A E, Dean N. Variable selection for model-based clustering[J]. Journal of the American Statistical Association, 2006, 101(473):168-178.
[10] Dean N, Raftery A E. Latent class analysis variable selection[J]. Annal Institute of Statistical Mathematics, 2010, 62:11-35.
[11] Akaike H. A new look at the statistical model identification[J]. IEEE Transactions on Automatic Control, 1974, 19(6):716-723.
[12] Burnham K P, Anderson D R. Multimodel inference understanding AIC and BIC in model selection[J]. Sociological Methods & Research, 2004, 33(2):261-304.
[13] Dash M, Liu H. Feature selection for classification[J]. Intelligent Data Analysis, 1997, 1(1-4):131-156.
[14] 李航. 统计学习方法[M]. 北京:清华大学出版社, 2012:11-14.
[15] Kursa M B, Rudnicki W. Feature selection with the Boruta package[J]. Journal of Statistical Software, 2010, 36(11):1-13.

面向配电网故障数据的BIC评估后向选择方法

An BIC Selection Method for Distribution Network Fault Data Feature Dimension Reduction

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