结合边缘计算的电力智慧中枢平台实时数据处理与分析系统设计  

Design of Real-time Data Processing and Analysis System for Power Intelligent Central Platform Combined with Edge Computing

作　　者：易鹏 YI Peng(Experimental Verification Center,State Grid Electric Power Research Institute Co.,Ltd.,Nanjing 210000,China)

机构地区：[1]国网电力科学研究院有限公司实验验证中心,江苏南京210000

出　　处：《科技创新与生产力》2024年第11期101-105,共5页Sci-tech Innovation and Productivity

摘　　要：本文指出在电力智慧中枢平台建设日益重要的背景下,其海量智能化高效处理需求变得尤为突出。为了满足这一需求,本文充分结合边缘计算的电力数据测量,通过边缘数据分析系统实时采集并分析用户电力数据,利用云-边协同架构优化任务分配,实现了时间与资源的高效利用。同时,引入了改进K-means算法对电力大数据进行分类处理,通过优化初始聚类中心,提高了算法处理大规模数据集的效率和准确性。根据改进后的算法,设计了电力智慧中枢平台数据分析系统,对系统的架构实施了全面分析,能够降低误分率。实验结果表明,本文提出的改进K-means算法的运算时间为20.15 s,明显优于其他算法,显示出良好的工程实用性。这一系统为智能电力的高效、稳定运行,提供了有力支持。This paper points out that in the context of the increasingly important construction of the power intelligent central platform,the demand for massive intelligent and efficient processing has become particularly prominent.In order to meet this demand,this paper fully combines the power data measurement of edge computing,collects and analyzes users'power data in real time through the edge data analysis system,and optimizes task allocation using the cloud edge collaborative architecture to achieve efficient use of time and resources.At the same time,an improved K-means algorithm is introduced to classify and process power big data.By optimizing the initial clustering center,the efficiency and accuracy of the algorithm in processing large-scale datasets are improved.Based on the improved algorithm,a data analysis system for the power intelligent central platform is designed,and a comprehensive analysis of the system architecture is conducted to reduce the misclassification rate.The experimental results show that the improved K-means algorithm proposed in this paper has a computation time of 20.15 seconds,which is significantly better than other algorithms and demonstrates good engineering practicality.This system provides strong support for the efficient and stable operation of intelligent power.

关 键 词：电力智慧中枢 数据分析系统 边缘计算 K-MEANS算法 误分率 

分 类 号：TM76[电气工程—电力系统及自动化] TP311.13[自动化与计算机技术—计算机软件与理论] TP301.6[自动化与计算机技术—计算机科学与技术]