- By:
- Vahedi, Soroush; Zhao, Junbo; Dong, Jin ; Wang, Bin; Lian, Jianming
- Page Number:
- 1-5
- Book Title:
- Proceedings of 2024 IEEE Power & Energy Society General Meeting (PESGM)
- Publication Date:
- March 12, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- 2024 IEEE Power & Energy Society General Meeting (PESGM)
- Conference Location:
- Seattle, Washington, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/PESGM51994.2024.10688425
Abstract
This paper presents a proactive strategy for hurricane-resilient distribution systems. It proposes a Bayesian Neural Network-based outage prediction model considering various parameters, including electrical components, and weather and environmental factors. Addressing challenges in imbalanced outage datasets, a Bias-Variance Tradeoff method is proposed. A resilience assessment model quantifies resilience indices, providing insights into system weaknesses. The approach identifies weak points and serves as a planning benchmark. Numerical results on the modified IEEE 123-node test system demonstrate effectiveness in realistic hurricane scenarios.
