A review of power system predictive failure model for resilience enhancement against hurricane events

Natural events such as hurricanes usually cause unimaginable destruction to the electric power system infrastructures across the globe leading to large‐scale power outages. While the transmission network offers relatively high resilience to the hurricane extreme wind speed intensity (HEWSI), the dis...

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Bibliographic Details
Published in:Journal of engineering (Stevenage, England) England), 2021-11, Vol.2021 (11), p.644-652
Main Authors: Omogoye, Okeolu Samuel, Folly, Komla Agbenyo, Awodele, Kehinde Oladayo
Format: Article
Language:English
Subjects:
Blackouts
Damage assessment
Disasters
Earthquakes
Electric power systems
Electricity
Energy resources
Hurricanes
Infrastructure
Outages
Prediction models
Recovery plans
Resilience
Transportation services
Wind speed
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Summary:Natural events such as hurricanes usually cause unimaginable destruction to the electric power system infrastructures across the globe leading to large‐scale power outages. While the transmission network offers relatively high resilience to the hurricane extreme wind speed intensity (HEWSI), the distribution power system network (DPSN) is always the worst hit. To enhance the DPSN against hurricane events, both the pre‐ and post‐event power system resilience enhancement techniques can be reviewed, and their limitations improved. Handling hurricane risks proactively for effective recovery plans requires rigorous techniques for locating and estimating the number of system component's damage causing outages on a DPSN. A review of the resilience evaluation methodologies utilized for a proactive statistical system component's failure predictive model is presented in this paper. As a contribution, this article presents the current practices, the problems, and points out the future research directions for a statistical system components’ line outage predictive model that can greatly enhance the DPSN against hurricane events for short‐term operational planning.
ISSN:2051-3305
2051-3305
DOI:10.1049/tje2.12092