A. Domijan, Jr., A. Islam, W.S. Wilcox, R.K. Matavalam, J.R. Diaz, L. Davis, and J. D'Agostini (USA)
Interruptions, Meteorology, Statistics, Modeling,Reliability.
In electric power distribution networks it has been seen
that weather plays an important role in the daily total
number of interruptions (N). It is the premise of this paper
that the effects of various weather parameters on power
reliability can be linearly modeled, and the model can be
used to account for up to 50% of the variance from the
mean of N. Regression models were developed using both
raw weather data and weather data that was modeled to
reflect their known effects on N. Because the R2
the regression result is the percentage of variance about
the mean that is accounted for by the equation, that value
was chosen as the statistic of interest.
This paper provides analysis of, and modeling for, the
power distribution system response to average
temperature (T), two minute maximum sustained wind
speed (S), daily total rainfall (R), and total daily number
of lightning strikes (LS). The results show that the
modeled equations return a consistently higher R2
than do equations that rely on raw weather data, and
consequently, account for a larger percentage of the
variance from the mean number of interruptions
experienced on a daily basis.