电网技术2017，Vol.41Issue(11)：3733-3740,8.DOI:10.13335/j.1000-3673.pst.2017.0141
考虑时间序列关联的变压器在线监测数据清洗
Online Monitoring Data Cleaning of Transformer Considering Time Series Correlation
林峻 1严英杰 1盛戈皞 1江秀臣 1杨祎 2陈玉峰2
作者信息
- 1. 上海交通大学电气工程系,上海市闵行区 200240
- 2. 国网山东省电力公司电力科学研究院,山东省济南市 250002
- 折叠
摘要
Abstract
A data cleansing strategy based on association rule analysis and neural network is proposed to solve the problem of data missing and abnormal data in process of large data state evaluation of transformer.Firstly, through mining of association rules, a mathematical model is established to measure correlation degree between state monitoring quantities, and a time sequence with strong correlation is established. Then density-based clustering algorithm is used to detect missing values and outliers in the sequence. Cleaning procedure and rule are proposed considering the sequence correlation to distinguish abnormal sensor data and equipment state anomaly. Wavelet neural network model is used for missing data prediction and error data correction for washable data points, dynamically modifying wavelet neural network parameters and combined forecasting. Cleaning efficiency and accuracy of the network are thus improved.Test results show that correlation analysis of sequence data and wavelet neural network can improve accuracy of online monitoring data cleaning of transformer based on actual monitoring data.关键词
大数据/异常检测/数据清洗/关联规则/小波神经网络Key words
large data/anomaly detection/data cleaning/association rules/wavelet neural network分类
信息技术与安全科学引用本文复制引用
林峻,严英杰,盛戈皞,江秀臣,杨祎,陈玉峰..考虑时间序列关联的变压器在线监测数据清洗[J].电网技术,2017,41(11):3733-3740,8.
