基于云计算平台Hadoop的线路参数并行辨识算法研究
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摘要
随着作为电网动态监测技术平台的广域量测系统(WAMS)在电网的应用普及,电网运行人员对于电网动态变化有了实时监测与分析的手段,但WAMS所产生的海量数据以及对于分析平台的高效率要求是WAMS应用的一大挑战。本文深入研究了基于Hadoop云计算平台的线路参数并行辨识算法,并提出算法的设计思路以及实现方法,为高效利用WAMS数据辨识线路参数给出了解决方法。对比实验证明基于云计算平台Hadoop的线路参数并行辨识算法大大提高线路参数辨识算法的计算效能,适合处理该应用中的WAMS海量数据。
As the power grid dynamic monitoring platform,the widely use of Wide-area Measurement System( WAMS) will help the grid operator monitor and analyze the dynamic changes in power grid operation. However,the huge dataset generated by WAMS and requirement of high efficient analysis platform are the challenges of WAMS applications. This paper has conducted thorough study of the parallel line parameter identification algorithm based on Hadoop platform,and provided the parallel algorithm design and implementation methods. It provides a efficient solution for line parameter identification using WAMS data. The comparison test demonstrates that the Hadoop platform based parallel line parameter identification algorithm will boost the efficiency greatly,and it is suitable for processing huge datasets.
As the power grid dynamic monitoring platform,the widely use of Wide-area Measurement System( WAMS) will help the grid operator monitor and analyze the dynamic changes in power grid operation. However,the huge dataset generated by WAMS and requirement of high efficient analysis platform are the challenges of WAMS applications. This paper has conducted thorough study of the parallel line parameter identification algorithm based on Hadoop platform,and provided the parallel algorithm design and implementation methods. It provides a efficient solution for line parameter identification using WAMS data. The comparison test demonstrates that the Hadoop platform based parallel line parameter identification algorithm will boost the efficiency greatly,and it is suitable for processing huge datasets.
引文
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[4]肖永,金小明,付超,等.基于广域量测系统的暂态失稳判别方法适应性研究[J].南方电网技术,2015,9(3)81-86.
[5]刘兆燕,江全元,曹一家.基于广域测量系统的快速暂态稳定预测方法[J].电力系统自动化,2007,31(21):1-4.
[6]赵菲,焦彦军,王铁强.基于WAMS的输电线路参数在线辨识的研究[J].电力科学与工程,2011,27(8):15-19.
[7]曲朝阳,朱莉,张士林.基于Hadoop的广域测量系统数据处理[J].电力系统自动化,2013,37(4):92-97.
[8]陈康,郑纬.云计算:系统实例与研究现状[J].软件学报,2009,20(5):1337-1348.
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[10]Dean J,Ghemawat S.Mapreduce:simplified data processing on large clusters[C].Proceedings of Operation System Design and Implementation.San Francisco,CA,2004:137-150.
[11]李刚,焦谱,文福拴,等.基于偏序约简的智能电网大数据预处理方法[J].电力系统自动化,2016,40(7):98-106.
[12]黄宏新,刘士源.交流电晕放电特性的影响因素研究[J].河北电力技术,2010,29(3):42-45.
[2]金基圣,牛夏牧.电网动态稳定实时监测系统[J].电力系统自动化,1999,23(10):17-19.
[3]宋方方,毕天姝,杨奇逊.基于WAMS的电力系统受扰轨迹预测[J].电力系统自动化,2006,30(23):27-32.
[4]肖永,金小明,付超,等.基于广域量测系统的暂态失稳判别方法适应性研究[J].南方电网技术,2015,9(3)81-86.
[5]刘兆燕,江全元,曹一家.基于广域测量系统的快速暂态稳定预测方法[J].电力系统自动化,2007,31(21):1-4.
[6]赵菲,焦彦军,王铁强.基于WAMS的输电线路参数在线辨识的研究[J].电力科学与工程,2011,27(8):15-19.
[7]曲朝阳,朱莉,张士林.基于Hadoop的广域测量系统数据处理[J].电力系统自动化,2013,37(4):92-97.
[8]陈康,郑纬.云计算:系统实例与研究现状[J].软件学报,2009,20(5):1337-1348.
[9]Ghemawat S,Gobioff H,Leung S.The google file system[J].SACM SIGOPS Operating System Review,2003,37(5):29-43.
[10]Dean J,Ghemawat S.Mapreduce:simplified data processing on large clusters[C].Proceedings of Operation System Design and Implementation.San Francisco,CA,2004:137-150.
[11]李刚,焦谱,文福拴,等.基于偏序约简的智能电网大数据预处理方法[J].电力系统自动化,2016,40(7):98-106.
[12]黄宏新,刘士源.交流电晕放电特性的影响因素研究[J].河北电力技术,2010,29(3):42-45.