基于大数据平台的电压质量诊断与应用分析

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。

Voltage quality diagnosis and application analysis based on big data platform

In view of the low efficiency of traditional voltage quality abnormal identification methods, it is difficult to achieve comprehensive identification and monitoring. This paper first builds a platform model for voltage quality diagnosis and analysis. Through principal component analysis, the main components of applications such as voltage monitoring and analysis of end users'' voltages and operation status analysis of distribution network low-voltage station areas are obtained. Cluster 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. Finally, 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 distribution network line fault judgment analysis.