基于Apache Spark的大数据电能质量干扰源分析  

Apache Spark-Based Big Data Power Quality Disturbance Source Analysis

作　　者：胡长武 李鹏 侯凯 HU Changwu;LI Peng;HOU Kai(Zhongwei Power Supply Company of State Grid Ningxia Electric Power Co.Ltd,Zhongwei Ningxia,755000,China)

机构地区：[1]国网宁夏电力有限公司中卫供电公司,宁夏中卫755000

出　　处：《自动化与仪器仪表》2024年第9期365-369,共5页Automation & Instrumentation

基　　金：国网宁夏电力有限公司科技项目资助《基于大数据技术的大型工业用户电能质量画像和增值服务策略研究》(5229ZW230003)。

摘　　要：电能质量监测规模的日益扩大导致电能质量数据的海量增加,现有的配电网电能质量监测系统难以实现大数据电能质量的有效分析。在此背景下,研究利用Apache Spark构建电能质量大数据计算框架,并以此设计了针对电能质量干扰源分析的大数据电能质量干扰源分析系统。系统验证分析显示,配电网电压变化主要是因为短时间的越限电压事件影响。不同方法对比显示,研究提出的系统精确率、召回率和F1值分别增加了0.37%、2.28%、1.32%。结果表明,研究提出的电能质量干扰源分析系统具有良好的分析能力,且0~4点的越上限电压事件和越下限电压事件是导致配电网电压变化的主要因素,电网公司应加强对该时段越限电压事件的关注并制定合理的防治与维护措施。The increasing scale of power quality monitoring leads to the massive increase of power quality data,and the existing distribution network power quality monitoring system is difficult to realize the effective analysis of big data power quality.In this context,the study utilizes Apache Spark to construct a big data computing framework for power quality,and in this way designs a big data power quality disturbance source analysis system for power quality disturbance source analysis.The system validation analysis shows that the voltage variation in the distribution network is mainly due to the effect of short duration voltage crossing events.Comparison of different methods shows that the accuracy,recall and F1 value of the proposed system increased by 0.37%,2.28%and 1.32%respectively.The results show that the proposed power quality disturbance source analysis system has good analyzing ability,and that the over-upper-limit voltage event and over-lower-limit voltage event at 0-4 points are the main factors leading to voltage changes in the distribution network,and the power grid company should pay more attention to the over-upper-limit voltage event at this time and formulate reasonable prevention,treatment and maintenance measures.

关 键 词：大数据 Apache Spark 电能电量 电压变化 干扰源分析 配电网 

分 类 号：TM60[电气工程—电力系统及自动化] TP202[自动化与计算机技术—检测技术与自动化装置]