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A new predictive risk-based framework is proposed to increase power distribution network resiliency by improving operator understanding of the energy interruption impacts. This paper expresses the risk assessment as the correlation between likelihood and impact. The likelihood is derived from the combination of Naive Bayes learning and Jenks natural breaks classifier. The analytics included in a GIS platform fuse together a massive amount of data from outage recordings and weather historical databases in just one semantic parameter known as failure probability. The financial impact is determined by a time series-based formulation that supports spatiotemporal data from fault management events and customer interruption cost. Results offer prediction of hourly risk levels and monthly accumulated risk for each feeder section of a distribution network allowing for timely risk mitigation.
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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.
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- Award ID(s):
- 2125985
- PAR ID:
- 10521487
- Publisher / Repository:
- IEEE ACCESS
- Date Published:
- Journal Name:
- IEEE Access
- Volume:
- 11
- ISSN:
- 2169-3536
- Page Range / eLocation ID:
- 113864 to 113875
- Subject(s) / Keyword(s):
- Customer notification, machine learning, outage mitigation, outage prediction, state of risk
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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A new predictive risk-based framework is proposed to increase power distribution network resiliency by improving operator understanding of the status of the grid. This paper expresses the risk assessment as the correlation between likelihood and impact. The likelihood is derived from the combination of Naive Bayes learning and Jenks natural breaks classifier. The analytics included in a GIS platform fuse together a massive amount of data from outage recordings and weather historical databases in just one semantic parameter known as failure probability. The financial impact is determined by a time series-based formulation that supports spatiotemporal data from fault management events and customer interruption cost. Results offer prediction of hourly risk levels and monthly accumulated risk for each feeder section of a distribution network allowing for timely tracking of the operating condition.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/ACCESS.2023.3324609
