基于云计算技术的电力大数据预处理属性约简方法

QU Zhaoyang

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基于云计算技术的电力大数据预处理属性约简方法

更新时间：2025-08-13

- 基于云计算技术的电力大数据预处理属性约简方法
- Automation of Electric Power Systems Vol. 38, Issue 8, Pages: 67-71(2014)
- 作者机构：
  
  东北电力大学信息工程学院,吉林市,吉林省,132012
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2014
- 稿件说明：
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QU Zhaoyang. [J]. Automation of electric power systems, 2014, 38(8): 67-71.
DOI：

QU Zhaoyang. [J]. Automation of electric power systems, 2014, 38(8): 67-71. DOI：

摘要

针对大数据时代下电网数据体量大、类型多、速度快的特点，传统的属性约简方法已经无法完成对电力大数据的预处理，为此提出一种基于云计算技术的电力大数据预处理属性约简方法。该方法剖析了粗糙集中相对正域理论的特性，利用MapReduce模型设计了可以并行计算正域中元素个数的属性约简算法MP_POSRS。最后，在Hadoop平台上对电网故障诊断表和风电实测数据进行属性约简，实验结果表明，该方法有效可行，并具有较好的加速比和可扩展性，适用于电力大数据预处理属性约简。

Abstract

In face of the conventional attribute reduction incapability of grid data preprocessing for its big volume

diversified types and high speed in the forthcoming age of big data

a new method of electric power big data preprocessing via attribute reduction based on cloud computing technology is put forward. The characteristics of the relative positive region theory of the rough set is analyzed

and a parallel attribute reducing algorithm named MP_POSRS that can calculate the number of elements in the relative positive region is designed by taking good advantages of the MapReduce model in this method. Finally

the experiments including operations on the decision table of power grid fault diagnosis and real data of wind power are performed on a Hadoop platform

the results showing that the method is effective and feasible for dealing with power grid big data

and with good speedup and scalability required by electric power big data preprocessing via attribute reduction.

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Related Institution

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