Coordenação Segura de Relés de Sobrecorrente usando Otimização por Coelhos Artificiais e Blockchain em Sistemas Elétricos com Recursos Energéticos Distribuídos

Detalhes bibliográficos
Ano de defesa: 2025
Autor(a) principal: Passatuto, Luiz Arthur Tarralo
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso embargado
Idioma: por
Instituição de defesa: Universidade Federal de Uberlândia
Brasil
Programa de Pós-graduação em Engenharia Elétrica
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://repositorio.ufu.br/handle/123456789/44919
http://doi.org/10.14393/ufu.di.2025.3
Resumo: The power system protection is essential to ensure safety, reliability, and continuity of energy supply, especially due to increasing digitalization and the integration of emerging technologies such as Distributed Energy Resources (DERs). However, the setting of inverse time overcurrent (TOC) units poses significant challenges related to device protective coordination in complex and dynamic scenarios. This study proposes an innovative methodology that combines the Artificial Rabbit Optimization (ARO) metaheuristic with Blockchain data processing technology, providing an optimized and secure solution for configuring the current and time parameters of the relays. The technique has been implemented in Python language, with a web interface developed using Streamlit, automating the adjustment process and enabling accessible data manipulation and an educational understanding of results. The investigation has been conducted by using Institute of Electrical and Electronic Engineers (IEEE) 13, 34, and 37-bus test feeders, integrating parallel processing into the metaheuristic execution. The results corroborate research on the low impact of inverter-based DERs on system short-circuit levels, while significantly affecting values associated with normal power flow, thus indicating the need for TOC parameter adjustments. The analyses also demonstrated that ARO exhibited high robustness in balancing exploration and exploitation, even when compared to well-established algorithms in academia (Genetic Algorithm, Differential Evolution and Particle Swarm Optimization), providing precise settings that enhance TOC selectivity and coordination, despite DER integration. Blockchain ensures secure and auditable management, promoting reliability and transparency in the application. The presented approach integrates cybersecurity and scientific innovation concepts, making it a promising solution to address the challenges of digitalization and the integration of new resources into the power systems.