基于偏序约简的智能电网大数据预处理方法

LI Gang

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基于偏序约简的智能电网大数据预处理方法

更新时间：2025-08-13

- 基于偏序约简的智能电网大数据预处理方法
- Automation of Electric Power Systems Vol. 40, Issue 7, Pages: 98-106(2016)
- 作者机构：
  
  华北电力大学控制与计算机工程学院,河北省,保定市,071003
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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LI Gang. [J]. Automation of electric power systems, 2016, 40(7): 98-106.
DOI：

LI Gang. [J]. Automation of electric power systems, 2016, 40(7): 98-106. DOI：

摘要

针对电力一次系统和电力信息系统的数据所具有的多维度、时空混杂等特征，建立了一种基于偏序约简的大数据属性约简预处理方法。该方法综合利用了MapReduce的可并行化优点，着眼于并发事件间的独立性，可以满足电力大数据属性维度与约简方面的覆盖要求。最后，分别以某光伏发电系统监测数据、变压器故障诊断数据和智能变电站通信系统实时性与可靠性预测数据为例，对属性约简进行模拟计算，并通过Hadoop平台进行测试，表明所提出的电力大数据属性约简方法性能优良。

Abstract

By taking into account the multi-dimensional

hybrid spatial and temporal characteristics of data from a power system and/or a power information system

a big data attribute reduction preprocessing method based on partial order reduction is presented. In the proposed method

the parallel advantage of MapReduce is employed and the independence of concurrent events is focused on

making it possible to meet the coverage requirements of big data attribute dimension and reduction in a power system. Finally

the proposed attribute reducing method is simulated by some examples including the monitoring data of a photovoltaic (PV) power generation system

the fault diagnosis data of a transformer

and real-time and reliability data of the communication system of an intelligent substation

with good performance observed through a Hadoop platform. This work is supported by National Natural Science Foundation of China (No. 51477151

No. 51407076)

Hebei Provincial Natural Science Foundation of China (No. F2014502050)

the Higher Education Scientific Research Project of Hebei Province (No. Z2013007)

and Fundamental Research Funds for the Central Universities (No. 2015ZD28).

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