配电网植被和天气引起的停电管理和缓解的风险预测状况

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access Pub Date : 2023-10-13 DOI:10.1109/ACCESS.2023.3324609

Rashid Baembitov;Mladen Kezunovic

{"title":"配电网植被和天气引起的停电管理和缓解的风险预测状况","authors":"Rashid Baembitov;Mladen Kezunovic","doi":"10.1109/ACCESS.2023.3324609","DOIUrl":null,"url":null,"abstract":"The paper proposes a novel approach for the outage State of Risk (SoR) assessment caused by weather and vegetation in the distribution grid. Machine Learning prediction algorithm is used in conjunction with GIS application for mapping the SoR for the entire network. The proposed optimization approach leads to the specification of the mitigation strategies that utility staff and customers can coordinate to minimize the impact of outages. The resulting SoR assessment enables the implementation of an innovative decision-making solution for utility operators, represented in the form of risk maps. Additionally, utilizing the SoR assessments, a Customer Notification System (CNS) is introduced to enhance customer awareness and facilitate the adoption of mitigation measures. This holistic approach shifts outage management from a reactive process to a proactive initiative, promoting grid resilience and reliability through planned outage mitigation.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"11 ","pages":"113864-113875"},"PeriodicalIF":3.4000,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6287639/10005208/10285092.pdf","citationCount":"0","resultStr":"{\"title\":\"State of Risk Prediction for Management and Mitigation of Vegetation and Weather Caused Outages in Distribution Networks\",\"authors\":\"Rashid Baembitov;Mladen Kezunovic\",\"doi\":\"10.1109/ACCESS.2023.3324609\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper proposes a novel approach for the outage State of Risk (SoR) assessment caused by weather and vegetation in the distribution grid. Machine Learning prediction algorithm is used in conjunction with GIS application for mapping the SoR for the entire network. The proposed optimization approach leads to the specification of the mitigation strategies that utility staff and customers can coordinate to minimize the impact of outages. The resulting SoR assessment enables the implementation of an innovative decision-making solution for utility operators, represented in the form of risk maps. Additionally, utilizing the SoR assessments, a Customer Notification System (CNS) is introduced to enhance customer awareness and facilitate the adoption of mitigation measures. This holistic approach shifts outage management from a reactive process to a proactive initiative, promoting grid resilience and reliability through planned outage mitigation.\",\"PeriodicalId\":13079,\"journal\":{\"name\":\"IEEE Access\",\"volume\":\"11 \",\"pages\":\"113864-113875\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2023-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/iel7/6287639/10005208/10285092.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Access\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10285092/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10285092/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

摘要

本文提出了一种新的方法来评估配电网中由天气和植被引起的停电风险状态。机器学习预测算法与GIS应用程序结合使用，用于映射整个网络的SoR。所提出的优化方法导致了公用事业员工和客户可以协调的缓解策略的规范，以最大限度地减少停电的影响。由此产生的SoR评估能够为公用事业运营商实施创新的决策解决方案，以风险图的形式表示。此外，利用SoR评估，引入了客户通知系统（CNS），以提高客户意识并促进采取缓解措施。这种整体方法将停电管理从被动过程转变为主动行动，通过计划内的停电缓解来提高电网的弹性和可靠性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

State of Risk Prediction for Management and Mitigation of Vegetation and Weather Caused Outages in Distribution Networks

The paper proposes a novel approach for the outage State of Risk (SoR) assessment caused by weather and vegetation in the distribution grid. Machine Learning prediction algorithm is used in conjunction with GIS application for mapping the SoR for the entire network. The proposed optimization approach leads to the specification of the mitigation strategies that utility staff and customers can coordinate to minimize the impact of outages. The resulting SoR assessment enables the implementation of an innovative decision-making solution for utility operators, represented in the form of risk maps. Additionally, utilizing the SoR assessments, a Customer Notification System (CNS) is introduced to enhance customer awareness and facilitate the adoption of mitigation measures. This holistic approach shifts outage management from a reactive process to a proactive initiative, promoting grid resilience and reliability through planned outage mitigation.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

期刊介绍： IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.