Research Article
Research on Abnormal Data Detection Method of Power Measurement Automation System
@INPROCEEDINGS{10.1007/978-3-030-67871-5_20, author={Ming-fei Qu and Nan Chen}, title={Research on Abnormal Data Detection Method of Power Measurement Automation System}, proceedings={Advanced Hybrid Information Processing. 4th EAI International Conference, ADHIP 2020, Binzhou, China, September 26-27, 2020, Proceedings, Part I}, proceedings_a={ADHIP}, year={2021}, month={2}, keywords={Power measurement automation system Abnormal data Detection method Iforest}, doi={10.1007/978-3-030-67871-5_20} }- Ming-fei Qu
Nan Chen
Year: 2021
Research on Abnormal Data Detection Method of Power Measurement Automation System
ADHIP
Springer
DOI: 10.1007/978-3-030-67871-5_20
Abstract
Aiming at the problems of long time consuming and low accuracy in traditional methods of abnormal data detection in power measurement automation system, this paper studies the methods of abnormal data detection in power measurement automation system. Design the data storage structure table of the electric power metering automation system database, and repair the missing data and denoise the data in the data table. Perform PAA calculation on the data to get the data feature sequence. After the P clustering algorithm pre-clusters the data, the iForest model is used to detect abnormal data to complete the research on the method. The experimental results show that the proposed detection method has the advantages of short detection time and high precision of 91.26–95.67%.
