微型电脑应用2024,Vol.40Issue(12) :106-109.

基于高精度重建模型的电力设备检修技术评估

Evaluation of Power Equipment Maintenance Technology Based on High-precision Reconstruction Model

张珍 林庆达 吴燕 段博
微型电脑应用2024,Vol.40Issue(12) :106-109.

基于高精度重建模型的电力设备检修技术评估

Evaluation of Power Equipment Maintenance Technology Based on High-precision Reconstruction Model

张珍 1林庆达 1吴燕 1段博1
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作者信息

  • 1. 广西电网有限责任公司南宁供电局,广西,南宁 530029
  • 折叠

摘要

随着大数据技术与电力设备信息化的建设和推广,为电力设备检修技术的评估提供了数据基础并带来了新的挑战.针对此,开展基于高精度重建模型的电力设备检修技术评估方法研究.在对生产管理系统中数据进行预处理和分析的基础上,应用极值分析理论处理边缘数据.同时,基于数据挖掘技术建立高精度重建模型,并将设备检修评估分为电气一次设备检修、高压试验,以及机械检修与维护3个模块,通过分析各模块的运行状况来反映设备的检修状态.实例分析结果表明,相比传统算法,所提方法在处理分析设备检修技术时效率更高、绝对误差更低,且绝对误差仅为3.158%,能够为运维检修人员提供虚拟的技术培训与评估服务.

Abstract

With the construction and promotion of big data technology and power equipment informatization,it provides a data basis and new challenges for the evaluation of power equipment maintenance technology.For this reason,this paper carries out the research on the evaluation method of power equipment maintenance technology based on high-precision reconstruction mod-el.On the basis of preprocessing and analyzing the data in the production management system,the extreme value analysis theo-ry is applied to process the edge data,and a high-precision reconstruction model is established based on data mining technology.The equipment maintenance evaluation is divided into three modules,i.e.,electrical primary equipment maintenance,high-voltage test,mechanical maintenance and repair.The maintenance status of the equipment is reflected by analyzing the opera-tion status of each module.The example analysis results show that the proposed method is more efficient in processing and ana-lyzing equipment maintenance technology,and the absolute error is only 3.158%,which can provide virtual technical training and evaluation services for operation and maintenance personnel.

关键词

高精度/重建模型/电力设备检修/模型设计/数据处理

Key words

high-precision/reconstruction model/power equipment maintenance/model design/data processing

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出版年

2024
微型电脑应用
上海市微型电脑应用学会

微型电脑应用

CSTPCD
影响因子:0.359
ISSN:1007-757X
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