Methodology for Obtaining a Tool for Detecting and Classifying Faults in Transmission Lines with High Integration of Wind Sources Interfaced by Inverters

• Grant number: 23/11816-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: February 01, 2024
• End date: January 31, 2025
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: Mário Oleskovicz
• Grantee: Daniel Fagundes Oliveira
• Host Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

The increasing incorporation of Inverted-Based Wind Resources (IBWRs) into power grids has imposed new challenges to traditional short-circuit classification methods since the characteristics of IBWRs contributions to faults will be influenced by the control method of the inverters used to interface the generators to the primary grid. In this context, investigative studies that cover the impacts of IBWRs in the current literature are still sporadic, and proposed solutions for such impacts in classification methods are scarce. To better analyze and solve problems, the increasing availability of data referring to transmission systems may enable the use of methods based on machine learning for classifying faults, since the existence of a vast database, whether resulting from measurements and/or computational simulations, will enable the training and testing of these methods. Thus, this project intends to propose an intelligent tool for classifying faults in systems with IBWRs, analyzing a wide range of intelligent methods, using signals generated from computer simulation of different fault scenarios, via PSCAD software, in a system with a typical topology for interconnecting IBWRs to the network. Finally, the performance of each of the proposed intelligent methods will be compared to determine which one is the most efficient for the test system in the presence of IBWRs.