状态检修大数据管理方法研究

Research on Large Data Management Method of Condition-based Maintenance

随着电网的快速发展,设备数量逐年增长,带电检测项目越来越多,检测数据格式越来越多样化,数据体量的增长给数据管理工作造成了不少困难,导致管理成本增加,也为设备状态评价和状态检修决策带来了困扰。现阶段,设备有关的停电试验、带电检测更多依赖于现场作业人员手工记录试验、检测数据,这种方式不便于对试验、检测数据进行归档、统计和分析。本文在积累了大量的带电检测数据和离线试验数据的基础上,设计了状态检修大数据管理平台,该平台系统具有良好的扩展性,具备目前带电检测和离线试验数据的基本管理功能,能够按照规程判据和专家经验实现对设备状态的自动分类,并提供开关柜历史检测数据分析、同一母线下开关柜检测数据横向对比、同型号开关柜检测数据离散度分析等专家分析功能,可以大幅提高数据管理效率,减轻一线班组工作压力,并为后续进行状态检修大数据分析奠定技术基础。

With the rapid development of power grid, the number of equipment is increasing year by year, more and more live detection items, more and more diversified detection data formats, the growth of data volume has caused many difficulties to data management, resulting in increased management costs, and also brought troubles to equipment condition assessment and condition-based maintenance decision-making. At present, the outage test and live detection of equipment depend more on manual recording of test and test data by field operators, which is not convenient for archiving, statistics and analysis of test and test data. On the basis of accumulating a large number of live detection data and off-line test data, this paper designs a large data management platform for condition-based maintenance. The platform system has good expansibility, has the basic management functions of live detection and off-line test data at present, and can realize automatic classification of equipment status according to rules and criteria and expert experience, and provides the historical detection number of switchgear cabinet. According to the expert analysis functions such as analysis, horizontal comparison of test data of switch cabinet under the same bus, and analysis of test data dispersion of switch cabinet of the same type, the data management efficiency can be greatly improved, the working pressure of first-line teams can be reduced, and the technical basis for subsequent analysis of large data of condition-based maintenance can be laid.