A review of power system predictive failure model for resilience enhancement against hurricane events. Issue 11 (29th October 2021)

• Record Type: Journal Article
• Authors: Omogoye, Okeolu Samuel; Folly, Komla Agbenyo; Awodele, Kehinde Oladayo
• Other Names: Lei Shunbo guestEditor.; Chen Chen guestEditor.; Hou Yunhe guestEditor.; Maharjan Sabita guestEditor.; Pozo David guestEditor.; Wang Zhaojian guestEditor.; Wu Qiuwei guestEditor.
• Is Part Of: Journal of engineering. Volume 2021:Issue 11(2021)
• Page Start: 644
• Page End: 652
• Publication Date: 2021-10-29
• Subjects: Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/tje2.12092
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract: 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.