Storm modeling for prediction of power distribution system outages

Utility companies spend tremendous amounts of money on storm outage restoration. A storm model which can accurately predict time-varying outages for approaching or ongoing storms will help with outage management. With such a model both customer down time and outage costs can be reduced. This paper proposes a two-stage prediction method to forecast storm related outages. In the first stage, historical outage and weather data are employed to create empirical models for different types of storms. In the second stage, observers are employed for tracking storm outages in real time. The effectiveness of the prediction model is evaluated with simulations applying actual storm data. Lightning is a primary cause of outages during summer storms. Results are presented on a possible relationship between flash density and outages.