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

• Main Authors: Arif I. Sarwat, Mohammadhadi Amini, Alexander Domijan, Aleksandar Damnjanovic, Faisal Kaleem
• Format: Article
• Language: English
• Published: IEEE 2016-01-01
• Series: Journal of Modern Power Systems and Clean Energy
• Subjects: Interruption prediction; Modeling; Artificial neural networks; Self-healing; Smart grid; Weather conditions effect
• Online Access: https://ieeexplore.ieee.org/document/8946803/

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.