Hurricanes have caused severe damage to the electric power system throughout
the Gulf coast region of the U.S., and electric power is critical to post-hurricane disaster
response as well as to long-term recovery for impacted areas. Managing hurricane risks
and properly preparing for post-storm recovery efforts requires rigorous methods for
estimating the number and location of power outages, customers without power, and
damage to power distribution systems. This dissertation presents a statistical power
outage prediction model, a statistical model for predicting the number of customers
without power, statistical damage estimation models, and a physical damage estimation
model for the gulf coast region of the U.S. The statistical models use negative binomial
generalized additive regression models as well as negative binomial generalized linear
regression models for estimating the number of power outages, customers without power,
damaged poles and damaged transformers in each area of a utility company’s service
area. The statistical models developed based on transformed data replace hurricane
indicator variables, dummy variables, with physically measurable variables, enabling
future predictions to be based on only well-understood characteristics of hurricanes. The
physical damage estimation model provides reliable predictions of the number of
damaged poles for future hurricanes by integrating fragility curves based on structural reliability analysis with observed data through a Bayesian approach. The models were
developed using data about power outages during nine hurricanes in three states served
by a large, investor-owned utility company in the Gulf Coast region.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2923 |
| Date | 15 May 2009 |
| Creators | Han, Seung Ryong |
| Contributors | Guikema, Seth |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
Page generated in 0.0019 seconds