Open AccessA Knowledge Base-based Method for Line Loss Abnormality Diagnosis in Station Area and Automatic Generation of Loss Reduction Measures
Author(s) -
Lei Ni,
Yao Li,
Qi Ding,
Jiangming Zhang
Publication year - 2022
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 - 10.1088/1742-6596/2218/1/012007
Subject(s) - abnormality , reduction (mathematics) , line (geometry) , computer science , hair loss , medicine , mathematics , geometry , dermatology , psychiatry
In view of the current low efficiency of line loss abnormality analysis and the inability to automatically generate loss reduction strategies, this paper proposes a knowledge base-based method for the abnormality diagnosis of line loss and automatic generation of loss reduction strategies. In this study, the research on line loss in station area at home and abroad is summarized, the classification of line loss abnormality is sorted out. Combined with the operation status of low-voltage distribution network and the current status of line loss, the intelligent diagnosis method of line loss abnormality is proposed and line loss reduction strategies is automatically generated according to the classification of line loss abnormality, which provide a decision-making support for improving the lean level of line loss control in the station area. Through application system development and display, the effectiveness of this method is verified.