摘要
设计并实现了一种基于大数据与云计算的配电网线损异常分析系统。该系统采用模块化设计,包括数据采集、存储、处理与分析、结果显示与预警四个核心模块。数据采集模块从配电网监测点收集实时运行数据,数据存储模块利用Hadoop平台进行高效存储与管理,数据处理与分析模块通过预处理、特征提取和K-means聚类算法识别线损异常。系统基于Python和Hadoop平台实现,测试结果表明,线损率预测值与实际值误差仅为0.1%,异常检测准确率达到96%。该系统能实时监测配电网运行状态,精准计算线损率并检测异常,为配电网的稳定运行和节能减排提供了有力技术支持。
This article designs and implements a distribution line loss anomaly analysis system based on big data and cloud computing.The system adopts a modular design,including four core modules:data acquisition,storage,processing and analysis,result display and early warning.The data collection module collects real-time operational data from distribution network monitoring points,and the data storage module utilizes the Hadoop platform for efficient storage and management.The data processing and analysis module identifies line loss anomalies through preprocessing,feature extraction,and K-means clustering algorithm.The system is implemented on Python and Hadoop platforms,and the test results show that the error between the predicted and actual line loss rates is only 0.1%,and the accuracy of anomaly detection reaches 96%.This system can monitor the real-time operation status of the distribution network,accurately calculate the line loss rate and detect anomalies,providing strong technical support for the stable operation and energy conservation and emission reduction of the distribution network.
作者
严怡然
YAN Yiran(Haimen District Power Supply Branch,Nantong 226100,China)
出处
《电工技术》
2025年第S1期235-236,239,共3页
Electric Engineering
关键词
配电网
线损异常分析
大数据
云计算
K-MEANS聚类
distribution network
abnormal analysis of line loss
big data
cloud computing
K-means clustering
