A Knowledge Base-based Method for Line Loss Abnormality Diagnosis in Station Area and Automatic Generation of Loss Reduction Measures

作者：Linna Ni, Yao Li, Qi Ding, Jiangming Zhang · 发表于：Journal of Physics Conference Series · 年份：2022 · DOI：10.1088/1742-6596/2218/1/012007 · 被引用次数：2 · 研究领域：Railway Systems and Energy Efficiency、Power Systems Fault Detection、Traffic Prediction and Management Techniques

Abstract 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.