基于大数据的配网智能融合终端运维信号异常辨识方法
Abnormal identification method of operation and maintenance signal of distribution network intelligent fusion terminal based on big data
畅楠1
作者信息
- 1. 国网陕西省电力有限公司兴平市供电分公司,陕西 兴平 712000
- 折叠
摘要
配网智能融合终端运维信号多样,数值范围和波动特性各异,增加了辨识难度.为提高精度,文章采用朴素贝叶斯算法进行大数据分析,通过预处理运维信号,包括噪声滤除和特征提取,将信号转化为适合异常辨识的形式.基于贝叶斯理论和特征条件独立假设,对信号进行并行化分类,实现异常运维信号的辨识.实验结果显示,该方法的相关系数高达0.968,证明了其辨识效果显著,为配网智能融合终端的运维管理提供了有力支持.
Abstract
The intelligent integration of distribution network terminal operation and maintenance signals is diverse,with different numerical ranges and fluctuation characteristics,which increases the difficulty of identification.To improve accuracy,this article adopts naive Bayesian algorithm for big data analysis.By preprocessing the operation and maintenance signal,including noise filtering and feature extraction,the signal is transformed into a form suitable for anomaly identification.Based on Bayesian theory and independent assumption of feature conditions,parallelized classification of signals is carried out to identify abnormal operation and maintenance signals.The experimental results show that the correlation coefficient of the design method is as high as 0.968,which proves its significant identification effect and provides strong support for the operation and maintenance management of intelligent integration terminals in the distribution network.
关键词
大数据/配网智能融合终端/运维信号/异常辨识Key words
big data/intelligent fusion terminal of distribution network/operation and maintenance signal/abnormal identification引用本文复制引用
出版年
2024
NETL
NSTL
万方数据