基于大数据分析的电网资产信息评估系统研究
|
摘要
为提高电网资产信息的管理和评估能力,建立电网资产信息评估系统,提出基于大数据分析的电网资产信息评估模型。采用大数据挖掘方法进行电网资产信息挖掘,结合模糊C均值聚类方法进行电网资产信息的特征融合处理,建立电网资源信息分布模型,采用关联特征分解方法进行电网资产信息的自适应重组,实现信息融合滤波检测,运用Lyapunov指数预测方法,实现电网资产信息预测与评估。在Linux内核下进行电网资产信息评估大数据分析算法加载,并在嵌入式环境下进行电网资产信息评估系统优化设计。测试结果表明,该系统进行电网资产信息评估的准确性较高,稳健性较好。
In order to improve the management and evaluation ability of power network asset information,establish the power network asset information evaluation system,In this paper,a power network asset information evaluation model based on big data analysis is put forward,which uses big data as mining method to mine power network asset information,and combines fuzzy C-means clustering method to deal with the feature fusion of power network assets information.The distribution model of power network resource information is established,the adaptive reorganization of power network asset information is carried out by using the method of correlation characteristic decomposition,the information fusion filter detection is realized,and the Lyapunov exponent prediction method is used.The big data analysis algorithm is loaded under the Linux kernel,and the optimization design of the power network asset information evaluation system is carried out under the embedded environment.The test results show that,the system has high accuracy and robustness in evaluating power network asset information.
In order to improve the management and evaluation ability of power network asset information,establish the power network asset information evaluation system,In this paper,a power network asset information evaluation model based on big data analysis is put forward,which uses big data as mining method to mine power network asset information,and combines fuzzy C-means clustering method to deal with the feature fusion of power network assets information.The distribution model of power network resource information is established,the adaptive reorganization of power network asset information is carried out by using the method of correlation characteristic decomposition,the information fusion filter detection is realized,and the Lyapunov exponent prediction method is used.The big data analysis algorithm is loaded under the Linux kernel,and the optimization design of the power network asset information evaluation system is carried out under the embedded environment.The test results show that,the system has high accuracy and robustness in evaluating power network asset information.
引文
[1]张建华,曾博,张玉莹,等.主动配电网规划关键问题与研究展望[J].电工技术学报,2014,29(2):13-23.
[2]DONG G L,RYU K S,BASHIR M,et al.Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction[J].Journal of Medical Systems,2013,37(2):1-10.
[3]KHALILI A,SAMI A.Sys Detect:a systematic approach to critical state determination for industrial intrusion detection systems using Apriori algorithm[J].Journal of Process Control,2015,2776:154-160.
[4]Mernik M,Liu S H,Karaboga M D,et al.On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation[J].Information Sciences,2015,291(10):115-127.
[5]赵学健,孙知信,袁源.基于预判筛选的高效关联规则挖掘算法[J].电子与信息学报,2016,38(7),1654,1659.
[6]黄创光,印鉴,汪静,等.不确定近邻的协同过滤推荐算法[J].计算机学报,2010,33(8),1369,1377.
[7]陈雪,黄智力,罗键.基于相对相似度关系的三角模糊数型不确定多属性决策法[J].控制与决策,2016,31(12),2232,2240.
[8]熊兴隆,杨立香,马愈昭,等.基于模糊C均值的低空风切变预警算法[J].计算机应用,2018,38(3):655-660.
[9]ZHENG J F,ZHANG J,ZHU K Y,et al.Gust front statistical characteristics and automatic identification algorithm for CINRAD[J].Acta Meteorologica Sinica,2014,28(4):607-623.
[10]HWANG Y,YU T Y,LAKSHMANAN V,et al.Neuro-fuzzy gust front detection algorithm with S-band polarimetric radar[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(3),1618,1628.
[11]王永强,周建中,莫莉,等.基于机组综合状态评价策略的大型水电站精细化日发电计划编制方法[J].电网技术,2012,36(7):94-99.
[12]张巍,阎立.下一代互联网在智能电网中的应用[J].电力系统通信,2012,33(242):95-100.
[13]黄伟,庞琳,曹彬,等.基于分区解耦的配电网状态估计的分布式并行计算[J].电力系统保护与控制,2014,42(15):45-51.
[14]Bliman P A,Ferrari-Trecate G.Average consensus problems in networks of agents with delayed communications[J].Automatica,2013,44(8),1985,1995.
[15]周双,冯勇,吴文渊,等.一种基于模糊C均值聚类小数量计算最大Lyapunov指数的新方法[J].物理学报,2016,65(2):020502.
[2]DONG G L,RYU K S,BASHIR M,et al.Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction[J].Journal of Medical Systems,2013,37(2):1-10.
[3]KHALILI A,SAMI A.Sys Detect:a systematic approach to critical state determination for industrial intrusion detection systems using Apriori algorithm[J].Journal of Process Control,2015,2776:154-160.
[4]Mernik M,Liu S H,Karaboga M D,et al.On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation[J].Information Sciences,2015,291(10):115-127.
[5]赵学健,孙知信,袁源.基于预判筛选的高效关联规则挖掘算法[J].电子与信息学报,2016,38(7),1654,1659.
[6]黄创光,印鉴,汪静,等.不确定近邻的协同过滤推荐算法[J].计算机学报,2010,33(8),1369,1377.
[7]陈雪,黄智力,罗键.基于相对相似度关系的三角模糊数型不确定多属性决策法[J].控制与决策,2016,31(12),2232,2240.
[8]熊兴隆,杨立香,马愈昭,等.基于模糊C均值的低空风切变预警算法[J].计算机应用,2018,38(3):655-660.
[9]ZHENG J F,ZHANG J,ZHU K Y,et al.Gust front statistical characteristics and automatic identification algorithm for CINRAD[J].Acta Meteorologica Sinica,2014,28(4):607-623.
[10]HWANG Y,YU T Y,LAKSHMANAN V,et al.Neuro-fuzzy gust front detection algorithm with S-band polarimetric radar[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(3),1618,1628.
[11]王永强,周建中,莫莉,等.基于机组综合状态评价策略的大型水电站精细化日发电计划编制方法[J].电网技术,2012,36(7):94-99.
[12]张巍,阎立.下一代互联网在智能电网中的应用[J].电力系统通信,2012,33(242):95-100.
[13]黄伟,庞琳,曹彬,等.基于分区解耦的配电网状态估计的分布式并行计算[J].电力系统保护与控制,2014,42(15):45-51.
[14]Bliman P A,Ferrari-Trecate G.Average consensus problems in networks of agents with delayed communications[J].Automatica,2013,44(8),1985,1995.
[15]周双,冯勇,吴文渊,等.一种基于模糊C均值聚类小数量计算最大Lyapunov指数的新方法[J].物理学报,2016,65(2):020502.