Open AccessResearch on Monitoring and Diagnosis Technology of Data Anomaly in Distribution Network
Author(s) -
Wensi Huang,
Xin Lü,
Jiandi Hu,
Qiangbin Ye
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/632/4/042008
Subject(s) - node (physics) , data mining , fault (geology) , adaptability , computer science , reliability (semiconductor) , visualization , feature (linguistics) , anomaly detection , anomaly (physics) , identification (biology) , real time computing , engineering , ecology , power (physics) , linguistics , physics , philosophy , botany , structural engineering , condensed matter physics , quantum mechanics , seismology , biology , geology
Aiming at the problems of a large amount of abnormal data in the distribution network and the poor adaptability of traditional distribution network data, the paper proposes an intelligent distribution network status and abnormal data monitoring method, which has undergone data pre-processing and data fusion, Data analysis and visualization, and state identification and processing, a total of 4 links, turning multiple electrical feature quantities into a single comprehensive feature quantity, monitoring the operation status of the distribution network, and according to the relationship between each node and the size of the local anomaly factor to achieve intelligence Judgment and location of distribution network fault areas. The thesis realizes the detection and location of faults according to the size of each node's LOF value and the node's association relationship. After RTDS semi-physical closed-loop test, the accuracy and reliability of fault determination and location are high, which has certain reference value.