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文章摘要
基于大数据平台的电压质量诊断与应用分析
Voltage quality diagnosis and application analysis based on big data platform
Received:June 18, 2020  Revised:November 20, 2021
DOI:10.19753/j.issn1001-1390.2023.10.018
中文关键词: 主成分分析  模糊聚类  电压质量  J2EE
英文关键词: principal component analysis, fuzzy clustering, voltage quality, J2EE
基金项目:
Author NameAffiliationE-mail
bao fu* Information center of Yunnan Power Grid Co., Ltd, hugu6788@163.com 
GaoYudou Information center of Yunnan Power Grid Co., Ltd, hugu6788@163.com 
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中文摘要:
      针对传统电压质量异常识别方法效率低下,难以做到全面的识别与监控的问题。文中搭建了电压质量诊断与分析平台模型,通过主成分分析得到影响端用户电压监测分析、配网低压台区运行状态分析等应用的主成分,对数据进行降维,实现数据的简化处理。聚类分析筛选出符合异常特征的电压异常数据,利用电压 异常模型确定异常数据,生成电压异常识别结果。基于J2EE技术框架在末端用户电压监测分析、配网低压台区运行状态分析、配网线路故障判断分析等方面进行可视化展示。
英文摘要:
      In view of the low efficiency of traditional voltage quality abnormal identification methods, it is difficult to achieve comprehensive identification and monitoring. This paper builds a platform model for voltage quality diagnosis and analysis. The principal components of applications such as voltage monitoring and analysis of voltages and operation status analysis of end users in distribution network low-voltage station areas are obtained through principal component analysis. Clustering analysis screens out abnormal voltage data that meets the characteristics of abnormal voltage, and uses abnormal voltage model to determine abnormal data to generate abnormal voltage recognition results. Based on the J2EE technical framework, visual display is made in terms of end-user voltage monitoring analysis, distribution network low-voltage station operation status analysis, and line fault judgment analysis of distribution network.
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