Document Type

Article

Publication Date

10-9-2025

Department

Department of Electrical and Computer Engineering

Abstract

In recent years, there has been an increase in the frequency of severe weather events (like hurricanes). These events are responsible for most power outages in power distribution systems (PDSs). Particularly susceptible to storms are overhead PDSs. In this study, the dynamic Bayesian network (DBN)-based failure model was developed for different hurricane scenarios to predict the line failure of overhead lines. Based on the outcomes of the DBN model, a service restoration model was formulated to maximize restored loads and minimize power losses using Particle Swarm Optimization (PSO)-based distributed generation (DG) integration and system reconfiguration. Three different case studies based on the IEEE 33 bus system were conducted. The overhead line failure prediction and service restoration model findings were further used to calculate resilience metrics. With reconfiguration the load restored from 90.3% to 100% for Case 1 and from 34.994% to 80.35% for Case 2. However, for Case 3, reconfiguration alone was not sufficient to show any improvement in performance. On the other hand, DG integration successfully restored load to 100% in all three cases. These results demonstrated that the combined DBN-based failure modeling and PSO-driven optimal restoration strategy under hurricane-induced disruptions can effectively strengthen system resilience.

Publisher's Statement

© 2025 by the authors. Published by MDPI on behalf of the International Institute of Knowledge Innovation and Invention. Licensee MDPI, Basel, Switzerland. Publisher’s version of record: https://doi.org/10.3390/asi8050149

Publication Title

Applied System Innovation

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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