L. Yao, L. Ni, Huanlei Yu, Qi Ding, Jianmin Zhang, Jiangming Zhang, An Wen
{"title":"Integrated decision-making for line loss online calculation and management based on situation awareness visualization","authors":"L. Yao, L. Ni, Huanlei Yu, Qi Ding, Jianmin Zhang, Jiangming Zhang, An Wen","doi":"10.1109/ICEMPE51623.2021.9509237","DOIUrl":null,"url":null,"abstract":"In view of the large amount of data in the low-voltage distribution network (LVDN) and the low efficiency of line loss analysis, this paper constructs an integrated decision-making platform for line loss online calculation and management based on situation awareness visualization technology. In this research, how to make full use of the existing system data to carry out the research on the intelligent diagnosis model of the line loss abnormality in the distribution network, and use the visualization method to realize the abnormality monitoring and automatically generate the loss reduction strategy is the goal of this article. First, through the user's electricity consumption data obtained from the electricity collection system; secondly, combine the user files of the marketing business application system, the geographic information of the GIS system and other data, and use big data analysis technology to realize the analysis and application of multi-source system data integration; Finally, develop main functions such as automatic identification of LVDN topology, online calculation and monitoring of line loss, situation awareness visualization of line loss in LVDN, intelligent diagnosis of line loss abnormalities and recommendation of loss reduction strategies. Applying human-computer interaction of situation awareness and visualization and abnormal diagnosis intelligence, intuitively and accurately display the line loss management problems in the distribution network. Through the test of specific examples, the effectiveness of the decision-making platform is verified.","PeriodicalId":7083,"journal":{"name":"2021 International Conference on Electrical Materials and Power Equipment (ICEMPE)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Electrical Materials and Power Equipment (ICEMPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEMPE51623.2021.9509237","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In view of the large amount of data in the low-voltage distribution network (LVDN) and the low efficiency of line loss analysis, this paper constructs an integrated decision-making platform for line loss online calculation and management based on situation awareness visualization technology. In this research, how to make full use of the existing system data to carry out the research on the intelligent diagnosis model of the line loss abnormality in the distribution network, and use the visualization method to realize the abnormality monitoring and automatically generate the loss reduction strategy is the goal of this article. First, through the user's electricity consumption data obtained from the electricity collection system; secondly, combine the user files of the marketing business application system, the geographic information of the GIS system and other data, and use big data analysis technology to realize the analysis and application of multi-source system data integration; Finally, develop main functions such as automatic identification of LVDN topology, online calculation and monitoring of line loss, situation awareness visualization of line loss in LVDN, intelligent diagnosis of line loss abnormalities and recommendation of loss reduction strategies. Applying human-computer interaction of situation awareness and visualization and abnormal diagnosis intelligence, intuitively and accurately display the line loss management problems in the distribution network. Through the test of specific examples, the effectiveness of the decision-making platform is verified.