大数据分析下低压配电台区线损自动测算方法研究
|
摘要
为了提高低压配电台区线损坏的定量分析和修复能力,提出一种基于大数据分析的低压配电台区线损自动测算方法。构建低压配电台区线损的定量回归分析模型,采用分段检验方法进行配电台区线损的大数据序列分析,构建区线损的大数据相空间重构模型,采用递归图谱分析方法进行低压配电台区线损自动测算,根据递归图谱中的规则性特征实现配电台区线损预测和度量。仿真结果表明,采用该方法进行低压配电台区线损测算的准确性较好,自动化和智能性水平较高,提高了低压配电台区线损的预测和修复能力。
In order to improve the quantitative analysis and repair ability of the low voltage distribution station area line damage,a method of automatic line loss calculation based on big data analysis is proposed.The quantitative regression analysis model of line loss in low voltage distribution station area is constructed.Big data sequence analysis of line loss in distribution station area is carried out by using subsection test method.The method of recursive graph analysis is used to calculate the line loss of low voltage distribution station area automatically.According to the regular characteristics of the recursive map,the line loss prediction and measurement of distribution station area is realized.The simulation results show that the method is more accurate and has a higher level of automation and intelligence,and improves the prediction and repair ability of the low voltage distribution station area line loss.
In order to improve the quantitative analysis and repair ability of the low voltage distribution station area line damage,a method of automatic line loss calculation based on big data analysis is proposed.The quantitative regression analysis model of line loss in low voltage distribution station area is constructed.Big data sequence analysis of line loss in distribution station area is carried out by using subsection test method.The method of recursive graph analysis is used to calculate the line loss of low voltage distribution station area automatically.According to the regular characteristics of the recursive map,the line loss prediction and measurement of distribution station area is realized.The simulation results show that the method is more accurate and has a higher level of automation and intelligence,and improves the prediction and repair ability of the low voltage distribution station area line loss.
引文
[1] 孙力娟,陈小东,韩崇,等.一种新的数据流模糊聚类方法[J].电子与信息学报,2015,37(7):1620-1625.
[2] 邢长征,刘剑.基于近邻传播与密度相融合的进化数据流聚类算法[J].计算机应用,2015,35(7):1927-1932.
[3] Marey M,Dobre O A,and Liao B.Classification of STBC system over frequency-selective channels[J].IEEE Transactions on Vehicular Technology,2015,64(5):2159-2164.
[4] YANG Z M,QIAO L Y,PENG X Y.Research on datamining method for imbalanced dataset based on i mproved SMOTE[J].Acta Electronica Sinica,2007,35(S2):22-26.
[5] GU Q,YUAN L,XIONG Q J,et al.A comparative study of cost-sensitive learning algorithm based on imbalanced data sets[J].Microelectronics and Computer,2011,28(8):146-149.
[6] MAO W T,TIAN Y Y,WANG J W,et al.Granular extreme learning machine for sequential imbalanced data[J].Control and Decision,2016,31(12):2147-2154.
[7] GU Q,YUAN L,NING B,et al.A noval classification algorithm for imbalanced datasets based on hybrid resampling strategy[J].Computer Engineering and Science,2012,34(10):128-134.
[8] GUO Huaping,ZHOU Jun,WU Chang’an,et al.k-nearest neighbor classification method for class-imbalanced problem.Journal of Computer Applications,2018,38(4):955-959.
[9] 庞伟林,林浩.基于动作回放的数据交互质量保障方法[J].自动化与仪器仪表,2017,(12):35-37.
[10] 侯晓芳,王欢,李瑛.一种基于HIVE和分布式集群的大量数据高效处理方法研究[J].中国电子科学研究院学报,2018,13(3):315-320.
[11] 侯森,罗兴国,宋克.基于信息源聚类的最大熵加权信任分析算法[J].电子学报,2015,43(5):993-999.
[12] 毕安琪,王士同.基于Kullback-Leiber距离的迁移仿射聚类算法[J].电子与信息学报,2016,38(8):2076-2084.
[13] 汪玉凤,段本涛,冯颖.级联H桥APF软启动方式的研究[J].电源学报,2018,16(4):157-161.
[14] 戴立坤.大型多媒体网络通信中的安全监测平台设计[J].现代电子技术,2016,39(24):9-13.
[15] 米捷,张鹏,于海鹏.粒子群差分扰动优化的聚类算法研究[J].河南工程学院学报,2016,28(1):63-68.
[2] 邢长征,刘剑.基于近邻传播与密度相融合的进化数据流聚类算法[J].计算机应用,2015,35(7):1927-1932.
[3] Marey M,Dobre O A,and Liao B.Classification of STBC system over frequency-selective channels[J].IEEE Transactions on Vehicular Technology,2015,64(5):2159-2164.
[4] YANG Z M,QIAO L Y,PENG X Y.Research on datamining method for imbalanced dataset based on i mproved SMOTE[J].Acta Electronica Sinica,2007,35(S2):22-26.
[5] GU Q,YUAN L,XIONG Q J,et al.A comparative study of cost-sensitive learning algorithm based on imbalanced data sets[J].Microelectronics and Computer,2011,28(8):146-149.
[6] MAO W T,TIAN Y Y,WANG J W,et al.Granular extreme learning machine for sequential imbalanced data[J].Control and Decision,2016,31(12):2147-2154.
[7] GU Q,YUAN L,NING B,et al.A noval classification algorithm for imbalanced datasets based on hybrid resampling strategy[J].Computer Engineering and Science,2012,34(10):128-134.
[8] GUO Huaping,ZHOU Jun,WU Chang’an,et al.k-nearest neighbor classification method for class-imbalanced problem.Journal of Computer Applications,2018,38(4):955-959.
[9] 庞伟林,林浩.基于动作回放的数据交互质量保障方法[J].自动化与仪器仪表,2017,(12):35-37.
[10] 侯晓芳,王欢,李瑛.一种基于HIVE和分布式集群的大量数据高效处理方法研究[J].中国电子科学研究院学报,2018,13(3):315-320.
[11] 侯森,罗兴国,宋克.基于信息源聚类的最大熵加权信任分析算法[J].电子学报,2015,43(5):993-999.
[12] 毕安琪,王士同.基于Kullback-Leiber距离的迁移仿射聚类算法[J].电子与信息学报,2016,38(8):2076-2084.
[13] 汪玉凤,段本涛,冯颖.级联H桥APF软启动方式的研究[J].电源学报,2018,16(4):157-161.
[14] 戴立坤.大型多媒体网络通信中的安全监测平台设计[J].现代电子技术,2016,39(24):9-13.
[15] 米捷,张鹏,于海鹏.粒子群差分扰动优化的聚类算法研究[J].河南工程学院学报,2016,28(1):63-68.