Weather-based interruption prediction in the smart grid utilizing chronological data

This unique study will demonstrate a combined effect of weather parameters on the total number of power distribution interruptions in a region. Based on common weather conditions, a theoretical model can predict inter- ruptions and risk assessment with immediate weather con- ditions. Using daily and hourly weather data, the created models will predict the number of daily or by-shift inter- ruptions. The weather and environmental conditions to be addressed will include rain, wind, temperature, lightning density,humidity,barometricpressure, snowand ice. Models will be developed to allow broad applications. Statistical and deterministic simulations of the models using the data col- lected will be conducted by employing existing software, and the results will be used to refine the models. Models devel- oped in this study will be used to predict power interruptions in areas that can be readily monitored, thus validating the models. The application has resulted indefiningthe predicted number of interruptions in a region with a specific confidence level. Reliability is major concern for every utility. Predic- tion and timely action to minimize the outage duration im- proves reliability. Use of this predictor model with existing smart grid self-healing technology is proposed.