A parallel evaluation method of health degree based on MapReduce for smart grid dispatching and control system

2017 9th International Conference on Advanced Infocomm Technology (ICAIT) Pub Date : 2017-11-01 DOI:10.1109/ICAIT.2017.8388919

Xinpeng Li, Jie Yang, H. Zhang, Xin Gao, Bo Yan, Q. Jia, Yongzheng Mu

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引用次数: 2

Abstract

In order to grasp the running status of the smart grid dispatching and control system in time, a parallel evaluation method of health degree based on MapReduce is proposed in this paper for the system's business complexity, complexity of evaluation indicators, large amount of evaluation calculation and requirement of evaluation time. Firstly, the health degree evaluation system which reflects the relationship between service level and priority is sorted out on the basis of D5000 system hierarchy analysis. Secondly, the health score of the system is calculated by modular MapReduce framework. In the first level MapReduce, Map task reads data and splits task. Reduce task calculates second-level indicators by hierarchical of D5000 system. In the second level MapReduce, it summaries score of second-level indicators in order to get the score of system and give the system status. Meanwhile, the adaptive task scheduling method ensure the system load balancing. Finally, the experiment platform of health evaluation of D5000 system is established, and the health evaluation experiment is carried out by the data of D5000 system. The result shows that the proposed method improves the efficiency of health calculation.

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基于MapReduce的智能电网调度控制系统健康度并行评估方法

为了及时掌握智能电网调度控制系统的运行状态，针对系统业务复杂、评估指标复杂、评估计算量大、评估时间要求高等特点，提出了一种基于MapReduce的健康度并行评估方法。首先，在D5000系统层次分析的基础上，梳理出反映服务水平与优先级关系的健康度评价体系。其次，采用模块化MapReduce框架计算系统的健康度评分;在第一级MapReduce中，Map任务读取数据并拆分任务。Reduce任务对D5000系统的二级指标进行分层计算。在第二级MapReduce中，对第二级指标的得分进行汇总，得到系统的得分，给出系统的状态。同时，自适应任务调度方法保证了系统的负载均衡。最后，建立了D5000系统健康评价实验平台，利用D5000系统的数据进行了健康评价实验。结果表明，该方法提高了健康度计算的效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 9th International Conference on Advanced Infocomm Technology (ICAIT)

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