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电力大数据:2019,22(11):-
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基于大数据平台的电力设备运作状态监测系统研究
王福生, 尹耕, 康晓东, 王圣杰, 陈峥
(国网宁夏电力有限公司信息通信公司 750001)
Research on Power Equipment Operation Monitoring System Based on Big Data Platform
Wang Fusheng, Yin Geng, Kang Xiaodong, Wang Shengjie, Chen Zheng
(Information Communication Company of Ningxia Electric Power Corp)
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投稿时间:2019-05-28    修订日期:2019-08-05
中文摘要: 针对目前电力设备运作状态监测系统焦点都在集群的硬件索引上,对电力设备的线状、性能、节点状况和指令反馈等数据层的监控不足,本文采用大数据技术对电力设备运作状态的显著性及统计因子变量进行相应的决策分析,建成一套通过主动监测设备潜在的运行状态变化的多元回归系统模型。从而有效解决了电力设备运行过程实时监控,潜在故障预警、评估、回溯管理等问题。通过宁夏电力公司的试运行结果表明该模型平台能预期捕获电力设备运作状态下的各项影响变量因素,对这些因素进行掌控将广泛应用于电力设备运作部署与运维。据此结论为该大数据平台的电力设备运作状态监测系统,可自定义设备运行分析报表,实现数据分析结果的随需展现,起到保障电力设备安全、可靠运作,具有一定的推广意义。
Abstract:In view of the current hardware equipment operation status monitoring system focus on the hardware index of the cluster, the monitoring of the data layer of the power equipment such as line, performance, node status and command feedback is insufficient. This paper uses the big data technology to significantly improve the operating status of the power equipment. Sexual and statistical factor variables are used for corresponding decision analysis to build a multivariate regression system model that actively monitors the potential operational state of the equipment. Thereby effectively solving the real-time monitoring of the operation process of the power equipment, potential failure warning, evaluation, back-track management and other issues. The test results of Ningxia Electric Power Company show that the model platform can expect to capture various influence variable factors under the operating state of power equipment. The control of these factors will be widely used in power equipment operation deployment and operation and maintenance. According to this conclusion, the power equipment operation status monitoring system of the big data platform can customize the equipment operation analysis report, realize the on-demand display of the data analysis result, and ensure the safe and reliable operation of the power equipment, which has certain promotion significance.
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基金项目:自然科学基金项目(61605001)
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