Assessment of Natural Hazards Impacts on Critical Infrastructure Systems

Reliable energy is a staple of modern society; without it, industry grinds to a halt, communication systems go silent, and the public’s welfare is at risk. In this dissertation, we will present newly developed tools to aid decision-support challenges at electric distribution utilities that must mitigate, prepare for, respond to and recover from severe weather. First, we show a performance evaluation of outage prediction models for storms of all types (i.e. blizzards, thunderstorms and hurricanes) and magnitudes (from 20 to \u3e15,000 outages). Second, we present an analysis that shows how incorporating high-resolution infrastructure, vegetation management and LiDAR-derived hazardous tree pixels (HazPix) data can improve the spatial accuracy of outage predictions during hurricanes. Third, we demonstrate how crew-related variables (i.e. the number of crews working), the peak number of customers affected, and estimates from the previously calibrated outage prediction model can be used to forecast the storm outage restoration duration (the time it takes to repair 99.5% of outages during a storm event). Lastly, we combine the three previous objectives into an evaluation of i) how a future Hurricane Sandy (strengthened from large-scale thermodynamic climate change) might impact outages in Connecticut; ii) how different vegetation management strategies can decrease outages; and iii) the number of restoration crews that would be needed to repair the future outages in a timely manner. Each of these sub-objectives can be used to motivate proactive storm resilience initiatives (such as increased vegetation management or infrastructure hardening). This research has the potential to be used for other critical infrastructure systems (such as telecommunications, drinking water and gas distribution networks), and can be readily expanded to the entire New England region to facilitate better planning and coordination among decision-makers when severe weather strikes

Weather-Based Damage Prediction Models for Electric Distribution Networks

From thunderstorms to hurricanes, electric distribution networks are subject to a wide range of warm weather storm events. Tropical Storm Irene (2011) and Hurricane Sandy (2012) are two events in recent memory that disrupted over half of The Connecticut Light and Power Company’s (CL&P) service territory, which left some customers without power for up to eleven days. This research study investigates a damage prediction framework for both thunderstorms and hurricanes that combines two generalized linear models to probabilistically determine the occurrence and extent of damages, known as trouble spots, to the overhead power distribution network. The models are inputted with high-resolution weather simulations from the Weather and Research Forecasting (WRF) Model along with distributed information on CL&P’s infrastructure, tree canopy density, and land cover data. The models were subjected to cross validation based on 30 major storm cases including the two tropical storms (Storm Irene and Hurricane Sandy), and exhibited a median percent error less than 30% for predicting the counts of trouble spots per event. Additionally, we explore an operational example of these models by using forecasts from 48 and 24 hours ahead of landfall by Hurricane Sandy to demonstrate how a real-time damage prediction system might operate

Synergistic Use of Nighttime Satellite Data, Electric Utility Infrastructure, and Ambient Population to Improve Power Outage Detections in Urban Areas

Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) can monitor power outages in disaster-affected areas through the identification of missing city lights. When combined with locally-relevant geospatial information, these observations can be used to estimate power outages, defined as geographic locations requiring manual intervention to restore power. In this study, we produced a power outage product based on Suomi-NPP VIIRS DNB observations to estimate power outages following Hurricane Sandy in 2012. This product, combined with known power outage data and ambient population estimates, was then used to predict power outages in a layered, feedforward neural network model. We believe this is the first attempt to synergistically combine such data sources to quantitatively estimate power outages. The VIIRS DNB power outage product was able to identify initial loss of light following Hurricane Sandy, as well as the gradual restoration of electrical power. The neural network model predicted power outages with reasonable spatial accuracy, achieving Pearson coefficients (r) between 0.48 and 0.58 across all folds. Our results show promise for producing a continental United States (CONUS)- or global-scale power outage monitoring network using satellite imagery and locally-relevant geospatial data