Understanding and predicting interruptions index of medium voltage customers using fully connected networks

• Ano de defesa: 2022
• Autor(a) principal: Rosa, Gabriel Augusto
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Engenharia Elétrica
• Palavras-chave em Português: Fully Connected Networks; Power Quality; Interruptions on Medium Voltages; Redes Totalmente Conectadas; Qualidade de Energia; Interrupções em Médias Tensões; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA; Engenharia elétrica; Redes elétricas - Análise; Energia elétrica - Distribuição; Falhas de energia elétrica; Redes neurais (Computação)
• Link de acesso: https://repositorio.ufu.br/handle/123456789/35125; https://doi.org/10.14393/ufu.di.2022.239

The losses caused by the lack of electricity typically exceed the cost of the electricity itself. Improving power quality is a way to reduce or avoid loss of production in the industry, prevent fires or explosions, and minimize damages to industrial equipment. Therefore, finding customers that probably will have interruptions in advance will generate value for both the company and customers. The purpose of this study is to analyze data from units that consume electricity using neural networks and decision trees, such as self-organizing maps, CHAID and CART, and using fully connected neural networks to predict the interruption index for the next year. The results reveal an important space for improvements such as the connection between non-compliance of established indicators over time and specific points of electrical network with problems. That way supports the concessionaries to manage their infrastructure to get a better quality of the electric power network.