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

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.

A Comparative Study Between Bayesian Network and Hybrid Statistical Predictive Models for Proactive Power System Network Resilience Enhancement Operational Planning

“…However, the proposed model in [21] was never tested with a proactive system optimization model for impact assessment as regards the reduction in the expected load loss. Other literature such as [16], [19], [22] also supported the proposed BN-DSN predictive model as a better operational planning decisionmaking tool for the distribution system operators (DSO) compared to the combined statistical DSN's line FC-MCS-SCENRED predictive algorithm. However, studies to corroborate or support this claim are largely unexplored.…”

“…The papers suggested the need to change the reactive operational resilience improvement techniques proposed by many researchers in assessing the resilience improvement of the DSN against hurricane events, to a more defensive proactive operational resilience improvement approach [14]. In Omogoye et al, [15], [16] the works of the literature showed that the prospective statistical regression methods such as the generalized linear model (GLM), generalized additive model (GAM), system treeapproach mining models (STMM), and power system topology (PST)-based resilience evaluation approach which include the predictive DSN line's FC-MCS have been utilized to assess the hurricane-caused DSN line faults in the past. The GLM, GAM, and STMM approaches are grouped as systemlevel damage predictive algorithms [17].…”

“…However, an increase in PSN line fault prediction approximation error limits their application for the short-term operational resilience improvement planning against the predicted oncoming hurricane event. Similarly, the topologybased PSN resilience evaluation approach which includes a combined statistical DSN line's FC-MCS predictive model is also known to introduce high computational complexity due to the use of 1000 or more predicted system line fault scenarios for a proactive stochastic system optimization problem formulation and simulation [15], [16], [18]. It is worth noting that in all the prospective methods aforementioned, the uncertain nature of the hurricane event on the PSN was not captured.…”