Optimization of Reliability in Electric Distribution Systems Using Artificial Intelligence Techniques

This research project addresses the critical improvement of reliability in electrical distribution systems through the integration of advanced Artificial Intelligence (AI) techniques. The central problem lies in the increasing complexity of electrical grids, the need to minimize outages, and the difficulty of efficiently managing failures and integrating renewable energy sources using traditional methods. The proposed solution involves developing a comprehensive AI framework that includes detailed analysis of historical data, implementation of predictive models based on machine learning for early fault detection, and the development of optimization algorithms to enhance preventive and corrective maintenance strategies. Furthermore, AI-driven dynamic management systems will be designed to adapt to real-time operational changes. This proactive approach is expected to overcome the limitations of conventional methodologies, resulting in a substantial improvement in operational reliability, efficiency, and cost reduction for the electrical sector. Expected outcomes include the publication of an indexed scientific article and the development of advanced technological tools applicable to electrical infrastructure.<br/><br/><b>Goal</b>: <br/>The general objective of this research project is to develop a comprehensive framework based on artificial intelligence to improve reliability in electrical distribution systems. The focus will be on the strategic application of machine learning algorithms, optimization techniques, and predictive analysis to proactively foresee and manage failure events, optimize maintenance, and dynamically adapt to network variations.<br/><br/><b>Research lines</b>: <br/>Reliability and quality of electrical energy