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 value of 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 value 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.
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