Diagnosis and Location of Electrical Faults in Electric Distribution Systems Using Artificial Intelligence Techniques

This research project, led by the Intelligent Electrical Networks Research Group (GIREI), focused on applying artificial intelligence to optimize fault diagnosis and localization in electrical distribution networks. Addressing the need for improved supply reliability, the team developed advanced algorithms integrating electrical signal analysis and deep learning, enabling faster and more accurate fault identification compared to traditional methods. The methodology included model validation through simulations in standard environments like IEEE, using specialized software tools. This approach not only advanced technical knowledge but also optimized corrective maintenance strategies. The project results are notable for high scientific productivity, with 16 articles published in indexed journals (SCOPUS/WoS). The impact extends to reduced operational costs for utility companies and improved quality of life for end-users by decreasing service interruptions. Furthermore, the project fostered the academic development of undergraduate and graduate students, consolidating effective knowledge transfer within the electrical sector.<br/><br/><b>Goal</b>: <br/>Develop and validate artificial intelligence-based models for the diagnosis and precise localization of faults in electrical distribution systems. The goal is to improve operational efficiency and power supply reliability through advanced machine learning techniques.<br/><br/><b>Research lines</b>: <br/>Reliability and quality of electrical energy