Physics-Based Reliability Assessment of Community-Based Power Distribution System Using Synthetic Hurricanes
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8, Issue 1
Abstract
Power distribution systems are very vulnerable during hurricane events. Failure of power distribution systems could bring significant disruptions to the community’s daily activities. Sparse historical hurricane data are insufficient to establish hurricane risk models. Therefore, it has been challenging to evaluate the pole-wire system’s performance during hurricane events under strong winds. In the present study, a probabilistic framework integrating hurricane risk modeling and physics-based analysis is proposed to assess the reliability of the power distribution system subjected to hurricane winds. Based on historical hurricane data, hurricane tracks are simulated using a modified statistical method by matching the synthetic data with the statistical characteristics from historical hurricanes facilitated by a copula model. Using a novel statistical model that implements a machine learning (ML) algorithm hurricane intensities are predicted. A hurricane risk model is established using the synthetic hurricane data. Fragility curves for each pole are obtained by physics-based Monte Carlo simulations facilitated by ML-based regression models instead of the empirical fitting model in order to incorporate the most influential factors. A surrogate model trained by the ML algorithm is employed to obtain the system fragility curve with a low computational cost. Finally, the annual failure probability of the pole-wire system could be obtained by integrating the annual hurricane wind speed probability density and the pole-wire system fragility curve.
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Data Availability Statement
Some data used during the study were provided by a third party. Direct requests for these materials may be made to the provider (Eversource Energy Center at the University of Connecticut) as indicated in the Acknowledgments. Some data are proprietary or confidential in nature and may only be provided with restrictions, such as the detailed dimensions and locations of the utility poles. Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request (data processing).
Acknowledgments
The authors gratefully acknowledge the data and support of Eversource Energy, Connecticut, and the Eversource Energy Center at the University of Connecticut. This support is greatly appreciated. The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of Eversource or any other agency.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8 • Issue 1 • March 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Mar 29, 2021
Accepted: Oct 6, 2021
Published online: Dec 17, 2021
Published in print: Mar 1, 2022
Discussion open until: May 17, 2022
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