Hurricane Risk Assessment of Power Transmission Systems: From Physics-Based Surrogates to Parameterized Fragility Models

@mastersthesis{wang2026,
  title = {Hurricane Risk Assessment of Power Transmission Systems: From Physics-Based Surrogates to Parameterized Fragility Models},
  author = {Wang, Xinyue},
  year = {2026},
  keywords = {Hurricane wind, Parameterized fragility, Power transmission system, Regional analysis, Risk Analysis, Transmission tower},
  abstract = {Electric power systems are vulnerable to hurricane-induced wind hazards, which have historically caused severe damage and widespread outages. Risk and resilience assessment of electric power networks requires estimating the structural response for thousands of structures at the regional scale. In particular, latticed transmission towers can entail large modeling and computational burden when high-fidelity finite element models are used. The lack of appropriate surrogate analytical models (or low-fidelity models) has hindered the efficient modeling of tower portfolios and, in turn, downstream research and applications.This dissertation advances a multi-scale framework for efficient and realistic hurricane risk assessment of transmission tower portfolios and networks. At the structural scale, panels are abstracted as the basic components in transmission towers, and their failure limit state and engineering demand parameters are defined to capture the buckling failure mode. Physics-based surrogate models for panels are developed, and polynomial response surface metamodels are constructed to ensure consistent simulation of panel capacity and demand. Validation against high-fidelity finite element models confirms that the proposed surrogate beam-column element formulation offers good accuracy and enables efficient panel-based tower analysis; for example, a pushover analysis can be completed within seconds. These surrogate models lay the foundation for deriving portfolio-oriented, parameterized fragility models for transmission towers.At the portfolio (or class) scale, the dissertation refines the framework for developing parameterized class fragility models by examining a range of modeling choices and proposing improvements to current practices. By applying this framework to a representative class of transmission towers in the Florida transmission line portfolio, parameterized hurricane fragility models are developed using efficient surrogate models and dynamic time history analysis. The results show that inter-structure variability constitutes the dominant source of uncertainty in the tower portfolio; prioritizing this uncertainty in propagation enables more efficient and effective fragility derivation. Sensitivity analysis reveals that tower fragilities are most influenced by three tuning parameters—design wind speed, span length, and yaw angle—as well as by several uncertain variables embedded within the fragility model. The derived fragility models fill the gap in parameterized models for transmission towers and can be tailored to support more granular hurricane risk assessments at the regional scale.At the network scale, the implications of adopting alternative fragility analysis practices for high-voltage transmission networks are examined. Within a probabilistic simulation framework, three representative fragility models for transmission towers—a parameterized class fragility model, a mean fragility model, and an archetype fragility model—are compared. Network performance is evaluated using multiple metrics such as global efficiency and the number of failed towers. For demonstration, the Florida transmission network is analyzed under two historical hurricanes—Irma (2017) and Michael (2018)—allowing for comparison with observed damage data. Results from both geospatial analyses and aggregate network-level metrics show that the parameterized fragility model consistently yields more reliable and accurate estimates of system performance. In contrast, using the mean fragility model tends to underestimate risk, while using one archetype model substantially overestimates it. These findings highlight the importance of adopting flexible, parameterized fragility models to capture the heterogeneity of transmission assets and to improve the accuracy of large-scale hurricane risk analyses. Such models enhance the fidelity of subsequent recovery and resilience analyses and ultimately support more informed and effective decision-making for power infrastructure under hurricane hazards.},
  language = {English},
}