Open AccessResearch on Multi- node Frame Early Warning System of Power Grid Based on Abnormal Data Extraction
Author(s) -
Bing Sui,
Xiaodong Chen,
Zhengwen Li,
Jun Zhao,
Jingfu Tian
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1654/1/012020
Subject(s) - computer science , frame (networking) , warning system , data collection , software , energy (signal processing) , node (physics) , data extraction , smart grid , real time computing , electric power system , data mining , power (physics) , engineering , electrical engineering , computer network , operating system , telecommunications , statistics , mathematics , structural engineering , medline , law , political science , physics , quantum mechanics
Nowadays, the collection of electrical energy measurement data is mainly completed by smart meters, electrical energy data monitoring equipment and electrical energy data management equipment. Due to system defects, equipment failures and human factors, it is prone to abnormal data collection. The design concept of online early warning system for extracting abnormal features of electrical energy data based on multi- node real-time computing framework is proposed in the paper to analyze the hardware and software of the online early warning system for extracting abnormal features of electrical energy data.