Abstract
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 interruptions and risk assessment with immediate weather conditions. Using daily and hourly weather data, the created models will predict the number of daily or by-shift interruptions. The weather and environmental conditions to be addressed will include rain, wind, temperature, lightning density, humidity, barometric pressure, snow and ice. Models will be developed to allow broad applications. Statistical and deterministic simulations of the models using the data collected will be conducted by employing existing software, and the results will be used to refine the models. Models developed 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 in defining the predicted number of interruptions in a region with a specific confidence level. Reliability is major concern for every utility. Prediction and timely action to minimize the outage duration improves reliability. Use of this predictor model with existing smart grid self-healing technology is proposed.
Highlights
Smart grid (SG) introduces a highly environmentallyfriendly context for the digital power customers
The results showed that the predictor was able to predict number of interruption for this large area analysis with good level of accuracy
Several models exist for extreme weather condition failure rates, and there are models for the baseline failure rates due to aging and other causes of equipment failure
Summary
Smart grid (SG) introduces a highly environmentallyfriendly context for the digital power customers. Received from NCDC, the total daily number of interruptions can be consistently stochastically predicted with an R2 value as high as 50% in simulations using actual interruption data as the target value [6] This indicates that weather has invisibly affected other cause codes. The researchers have found that, when daily weather variables are considered in the modeling function, the resulting forecasts of the daily number of interruptions can be stochastically predicted with an R2 value in the neighborhood of 50% in simulations using actual interruption data as the target value [9]
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