The power grid is inherently vulnerable to extreme climate and weather hazards, such as hurricanes. The overarching goal of this project was to develop hurricane fragility models for transmission towers and ways to improve their reliability. Toward this goal, the project introduced many research contributions. Key contributions and their significance are described below.

A primary objective was to develop reliable computational models for transmission towers. Existing approaches were not able to fully capture complex tower behavior under hurricanes including buckling of elements, failure of joints and the post-buckling performance of towers. Moreover, contributions of these and other complexities in the presence of uncertainties were not known. We developed an approach to modelling lattice transmission towers that captures buckling and post-buckling and joint slippage and failure, and subsequently analyzed their effects, while considering uncertainties. The type and location of damaged components in a transmission tower derived from 200 pushover analyses are depicted in Figure 1. Results indicated the high significance of buckling compared to joint slippage and the relatively rare occurrence of connection failures.

Another objective was to conduct aeroelastic wind tunnel tests on multi-span transmission systems at NSF NHERI Wall of Wind Experimental Facility (WOW EF) to investigate system-level behavior and validate the developed computational models. We conducted tests on a 1:50 scale aeroelastic model of a lattice tower and a transmission system consisting of three towers. Figure 2 shows sensor locations for a single tower. Figure 3 depicts the complete transmission lines model on the WOW turntable. Two techniques were utilized to evaluate along-wind and crosswind aerodynamic damping and compare with theoretical estimates. The responses of the single tower and multi-span transmission lines were also compared. The coupling effects seem to considerably change the aerodynamic damping of the system, compared to the single lattice tower. Furthermore, we estimated local drag coefficients and gust response factors over the height of the tower using an approach based on Kalman filtering, which facilitated fusion of noisy measurements from multiple sensors. The derived estimates were used in a Bayesian regression model to provide new recommendations. Results indicated that the existing equations in ASCE No. 7 and 74 for drag coefficient and gust response factor may underestimate wind loads by as much as 12% and 13%, respectively.

Reliability assessment of transmission systems was another major objective. These analyses require numerous high-fidelity simulations, which can be prohibitively time-consuming. We developed reliability analysis through Error rate-based Adaptive Kriging (REAK) that guides sampling and evaluates the accuracy based on the maximum error rate for failure probability estimates. Results indicated that REAK can reduce the computational demand by as much as 50% compared to other state-of-the-art methods. In addition, estimation of the accuracy of extant techniques when the true failure probability is unknown was an important challenge. We developed analytical confidence intervals (CIs) for failure probability estimates. We further extended this approach and proposed an adaptive multi-fidelity Gaussian process for reliability analysis when multi-fidelity models are available. With a high-fidelity model of the transmission tower and a low-fidelity one, the proposed method was able to efficiently construct a multi-fidelity model for the reliability analysis, while other state-of-the-art methods were not able to provide reliable estimates with the same computational cost. 

A key element of grid risk analysis is the ability to project the future state of the power grid when facing hurricanes. We established a set of performance-based limit states for lattice transmission towers subject to wind-induced extreme loadings. Specifically, five damage states including no damage, slight, moderate, and extensive damage, and collapse were defined. These limit states are founded on the nonlinear behavior of lattice transmission towers and the type and severity of failures in tower elements and connections, as they relate to the repair or replacement requirements of towers. Figure 4 shows the identification of the collapse state in the longitudinal direction of the lattice transmission tower. Fragility models based on the survival-failure outcomes of many nonlinear pushover analyses were subsequently generated using a logistic regression model. The outputs of this regression model, which are the probabilities of the transmission tower being in a particular damage state, are illustrated in Figure 5. We further applied a Lasso-logistic model to perform sensitivity studies and identify the relative importance of input parameters for the fragility models.

Finally, we developed a reliability-based design optimization (RBDO) approach to explore hardening of transmission structures. To overcome the high computational costs of RBDO for complex structures such as transmission towers, we proposed a quantile-based sequential method using surrogate models. The approach was applied to the design of a lattice tower with the objective of minimizing the costs under the reliability constraints for the tower and conductors. Results indicated that the proposed method can provide optimal designs that satisfy the probabilistic constraints while other state-of-the-art methods fail to converge.

Last Modified: 09/05/2021
Modified by: Abdollah Shafieezadeh