Fault Classification in Electrical Substations Using Signal Processing

This research project focuses on addressing the critical challenge of reducing power supply interruption times in substations, a key component of the transmission grid. The main problem lies in the slowness of traditional fault classification methods, which is unsustainable for the evolution towards smart electrical grids. The proposed solution involves applying advanced signal processing techniques to develop and evaluate faster and more accurate fault classification algorithms. The methodology includes collecting electrical data, modeling and simulating the substation using specialized software, creating a database from the obtained descriptors, and testing algorithms in MATLAB®. Performance will be evaluated through an experimental approach, comparing the accuracy of the developed methods against baseline conditions. The expected impact is organizational, enabling electric utility companies to decrease their Total Interruption Times (TIT) and improve service quality, benefiting society at large. Academic outcomes include the publication of two scientific articles indexed in Scopus.<br/><br/><b>Goal</b>: <br/>To classify faults in power electrical substations based on advanced signal processing techniques to improve efficiency and reduce power supply interruption times.<br/><br/><b>Research lines</b>: <br/>Planning and management of electrical systems