APPLICATION OF ARTIFICIAL INTELLIGENCE IN PREDICTING FAILURES IN ELECTRICAL NETWORKS

Abstract

The growing complexity of modern electrical networks, combined with the increasing energy demand and the integration of renewable sources, requires more efficient solutions to ensure reliability and continuity of power supply. In this context, Artificial Intelligence (AI) emerges as a promising tool for predicting and detecting failures in electrical systems, allowing proactive actions and reducing corrective maintenance costs. This work aims to apply AI techniques, especially machine learning algorithms and artificial neural networks, to predict failures in electrical distribution networks. The research proposes the use of predictive models based on historical operational and maintenance data to identify patterns and anomalies preceding failures. The methodology involves collecting operational data, preprocessing information, modeling algorithms, and evaluating model performance. It is expected that AI can increase accuracy in failure prediction, contribute to predictive maintenance, and reduce interruptions in power supply.