  The reliable power system operation is a major goal for electric utilities,
which requires the accurate reliability forecasting to minimize the duration of
power interruptions. Since weather conditions are usually the leading causes
for power interruptions in the smart grid, especially for its distribution
networks, this paper comprehensively investigates the combined effect of
various weather parameters on the reliability performance of distribution
networks. Specially, a multilayer perceptron (MLP) based framework is proposed
to forecast the daily numbers of sustained and momentary power interruptions in
one distribution management area using time series of common weather data.
First, the parametric regression models are implemented to analyze the
relationship between the daily numbers of power interruptions and various
common weather parameters, such as temperature, precipitation, air pressure,
wind speed, and lightning. The selected weather parameters and corresponding
parametric models are then integrated as inputs to formulate a MLP neural
network model to predict the daily numbers of power interruptions. A modified
extreme learning machine (ELM) based hierarchical learning algorithm is
introduced for training the formulated model using realtime reliability data
from an electric utility in Florida and common weather data from National
Climatic Data Center (NCDC). In addition, the sensitivity analysis is
implemented to determine the various impacts of different weather parameters on
the daily numbers of power interruptions.
