Short-term prediction of power outages in electrical distribution networks

Abstract

Predictive maintenance and reliability engineering are critical in industrial settings to enhance equipment performance and minimize unplanned downtime. This research, conducted within the machine learning framework, presents innovative solutions to the challenging problem of equipment failure prediction. The study creatively utilizes extensive datasets, including equipment records, weather conditions, and maintenance logs, to develop robust predictive models. Two distinct machine learning models are established for equipment and cables/lines, addressing the intricacies of class imbalances and missing data attributes. Model refinement, feature engineering, and interdisciplinary collaboration enhance predictive accuracy, precision, and recall. Notably, this research highlights the creative application of engineering knowledge and data science techniques, reasoning about complex equipment systems, and the importance of problem decomposition. The outcomes underscore the potential for real-time predictive maintenance in industrial contexts, offering substantial cost savings and improved equipment reliability. This research contributes to the evolving field of predictive maintenance and paves the way for future innovations in reliability engineering.