摘要
随着电力大数据时代的到来,数据量的骤增使得维护决策者所面临的决策环境与决策问题变得愈发复杂。针对这一问题,在云计算技术和设备状态维护技术的基础上,对云平台下的设备状态维护系统进行研究,构建基于云计算的全维度设备状态维护系统整体框架,并建立系统维护模型,针对遗传算法在求解实际问题时,计算能力难以满足人们需要的不足,引入Map Reduce技术,结合遗传算法和Map Reduce技术进行并行设计,并通过对比实验分析系统性能,此研究工作为我国电力设备状态维护系统的发展提供了参考和借鉴。
With the advent of the era of power large data,the sudden increase of data makes the decision-making environment and decision-making problems faced by maintenance decision-makers more complex.Aiming at this problem,this paper studies the device status maintenance system based on cloud computing technology and device status maintenance technology,constructs the overall framework of the full-dimension device status maintenance system based on cloud computing,and establishes the system maintenance model.Map Reduce technology is introduced to solve the problem that the genetic algorithm cannot meet the needs of people in solving practical problems,combining genetic algorithm and Map Reduce technology to design in parallel,and the system performance is analyzed by comparing experiments.The research work of this paper provides reference for the development of status maintenance system of power equipment in China.
作者
党晓婧
邓世聪
吕启深
许德成
刘伟斌
Dang Xiaojing;Deng Shicong;Lv Qishen;Xu Decheng;Liu Weibin(Shenzhen Power Supply Co.9 Ltd.,Shenzhen 518000,Guangdong,China;Shenzhen Comtop Information Technology Co.,Ltd.,Shenzhen 518034,Guangdong,China)
出处
《电测与仪表》
北大核心
2020年第5期8-13,共6页
Electrical Measurement & Instrumentation
基金
南方电网科技项目(090000GS62161590)。
