- Large-Scale Computational and Experimental Analysis and Design of Smart Control Systems
- Risk Management of Deteriorating Power Distribution Assets against Hurricanes
- Storm Surge Risks to Flood Defense Systems and Coastal Communities
- Hurricane Resilience Enhancement of Transmission Line Systems
- A Bridge Condition Index for Transportation Asset Management in Ohio
- Bayesian Methods for Prediction of Deterioration in Concrete Bridge Decks Using Visual Inspection Data
- Novel Fractional Order Ground Motion Intensity Measures for High Confidence Risk Assessment of Distributed Infrastructures
- Probabilistic Modeling of Corrosion Deterioration and Aging in Structures
- Seismic Fragility and Risk Analysis of Wharves in Liquefiable Soils
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Risk Management of Deteriorating Power Distribution Assets against Hurricanes
Distribution of electricity in the United States relies on overhead lines supported by wood poles. This distribution system suffers damage in high wind events resulting in interruption in electrical power to businesses and residencies. The wood poles suffer from the combined effect of aging and increasing stress from wind hazards leading to an unreliable and potentially unsafe distribution infrastructure. Over 100 million distribution poles, which are part of the critical energy delivery infrastructure, suffer immensely during wind storms. Current methods for predicting the distribution of times-to-failure of a general set of power system components do not address simultaneous effects of aging and hazards with a holistic view. Research on age-at-failure of poles has been sparse, incomplete, and inconsistent. This results in increased uncertainties in predictions of pole lifetimes, thus obstructing informed and accurate decision-making on effective pole management strategies.
The project proposes the development of stochastic Finite Element models for distribution poles integrated with time-dependent decay models to simulate the current and future state of the response of distribution poles to various climate conditions. Epistemic uncertainties will be reduced and various sources of aleatoric uncertainties in capacity and demand modeling will be captured. A novel strategy utilizing n-dimensional fragility surfaces integrated with regional hurricane hazard models will be merged to estimate failure probabilities, develop predictive models for the time-to-failure of distribution poles, and quantify the confidence levels in the predictions. A priority-based diagnostics and risk-informed maintenance framework will be developed on the basis of stochastic optimization for distribution networks that takes into account both the geographic exposure of the physical components to stressors and their importance for the reliability of the networks. The success afforded by this approach does not guarantee failure avoidance, but points to the regions in networks where use of well-maintained infrastructure may lower the risk of failures during storms and also provides guidance in selecting the poles for preventive replacement under normal operating circumstances.