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 approac...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10285092/ |
| _version_ | 1797655668759461888 |
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| author | Rashid Baembitov Mladen Kezunovic |
| author_facet | Rashid Baembitov Mladen Kezunovic |
| author_sort | Rashid Baembitov |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T17:17:52Z |
| format | Article |
| id | doaj.art-c5b26f7d1b284001bea47b5c859a77a7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T17:17:52Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c5b26f7d1b284001bea47b5c859a77a72023-10-19T23:01:28ZengIEEEIEEE Access2169-35362023-01-011111386411387510.1109/ACCESS.2023.332460910285092State of Risk Prediction for Management and Mitigation of Vegetation and Weather Caused Outages in Distribution NetworksRashid Baembitov0https://orcid.org/0000-0002-6515-8007Mladen Kezunovic1https://orcid.org/0000-0002-8925-4415Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USADepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USAThe 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.https://ieeexplore.ieee.org/document/10285092/Customer notificationmachine learningoutage mitigationoutage predictionstate of risk |
| spellingShingle | Rashid Baembitov Mladen Kezunovic State of Risk Prediction for Management and Mitigation of Vegetation and Weather Caused Outages in Distribution Networks IEEE Access Customer notification machine learning outage mitigation outage prediction state of risk |
| title | State of Risk Prediction for Management and Mitigation of Vegetation and Weather Caused Outages in Distribution Networks |
| title_full | State of Risk Prediction for Management and Mitigation of Vegetation and Weather Caused Outages in Distribution Networks |
| title_fullStr | State of Risk Prediction for Management and Mitigation of Vegetation and Weather Caused Outages in Distribution Networks |
| title_full_unstemmed | State of Risk Prediction for Management and Mitigation of Vegetation and Weather Caused Outages in Distribution Networks |
| title_short | State of Risk Prediction for Management and Mitigation of Vegetation and Weather Caused Outages in Distribution Networks |
| title_sort | state of risk prediction for management and mitigation of vegetation and weather caused outages in distribution networks |
| topic | Customer notification machine learning outage mitigation outage prediction state of risk |
| url | https://ieeexplore.ieee.org/document/10285092/ |
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