基于K均值聚类算法的电源运行状态数据分析  

Data Analysis of Power Supply Operating Status Based on K-Means Clustering Algorithm

作　　者：张鸿雁[1] ZHANG Hongyan(Department of Information Technology and Engineering,Jinzhong University,Jinzhong 030619.China)

机构地区：[1]晋中学院信息技术与工程系,山西晋中030619

出　　处：《电声技术》2022年第1期85-87,共3页Audio Engineering

基　　金：晋中学院“1331工程”重点创新团队建设计划项目;晋中学院精品课程(No.kc201920);晋中学院教学改革创新项目(No.Jg202050);山西省高等学校教学改革创新项目(No.J2020314)。

摘　　要：传统的电源运行监测方法存在监测准确率低的问题,因此提出基于K均值聚类算法的电源运行状态数据分析法。通过采集电源运行数据样本,并对电源样本数据进行清洗,去除冗余数据,对清洗后的数据进行归一化处理,运用K均值聚类算法对电源运行数据进行分类分析。通过对比实验,证明设计的电源运行状态数据分析方法分析准确率较高,具有实用性。The traditional power supply operation monitoring method has the problem of low monitoring accuracy. Therefore, a power supply operation state data analysis method based on the K-means clustering algorithm is proposed. By collecting power supply operating data samples, cleaning the power supply sample data, removing redundant data, normalizing the cleaned data, using the K-means clustering algorithm to classify and analyze the power supply operating data. Through comparative experiments, it is proved that the designed power supply operating state data analysis method has higher analysis accuracy and practicality.

关 键 词：聚类算法 归一化处理 电源样本 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]