Estimation of Total Real and Reactive Power Losses in Electrical Power Systems via Artificial Neural Network

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorda Silva, Giovana Gonçalves-
Autor(es): dc.creatorde Queiroz, Alexandre-
Autor(es): dc.creatorGarbelini, Enio-
Autor(es): dc.creatordos Santos, Wesley Prado Leão-
Autor(es): dc.creatorMinussi, Carlos Roberto-
Autor(es): dc.creatorBonini Neto, Alfredo-
Data de aceite: dc.date.accessioned2025-08-21T21:18:08Z-
Data de disponibilização: dc.date.available2025-08-21T21:18:08Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-06-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/asi7030046-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/303244-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/303244-
Descrição: dc.descriptionTotal real and reactive power losses in electrical power systems are an inevitable phenomenon and occur due to several factors, such as conductor resistance, transformer impedance, line reactance, equipment losses, and phase unbalance. Minimizing them is crucial to the system’s efficiency. In this study, an artificial neural network, specifically a Multi-layer Perceptron, was employed to predict total real and reactive power losses in electrical systems. The network is composed of three layers: an input layer consisting of the variables loading factor, real and reactive power generated on the slack bus, a hidden layer, and an output layer representing the total real and reactive power losses. The training method used was backpropagation, adjusting the weights based on the desired output. The results obtained, using datasets from IEEE systems with 14, 30, and 57 buses, showed satisfactory performance, with a mean squared error of around 10−4 and a coefficient of determination (R2) of 0.998. In validation with 20% of the data that was not part of the training, the network demonstrated effectiveness, with a mean squared error around 10−3. This indicates that the network was able to accurately predict total power losses based on loads, generating estimates close to the desired values.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionSchool of Engineering São Paulo State University (Unesp), SP-
Descrição: dc.descriptionSchool of Sciences and Engineering São Paulo State University (Unesp), SP-
Descrição: dc.descriptionSchool of Engineering São Paulo State University (Unesp), SP-
Descrição: dc.descriptionSchool of Sciences and Engineering São Paulo State University (Unesp), SP-
Descrição: dc.descriptionCAPES: 88887.956029/2024-00-
Idioma: dc.languageen-
Relação: dc.relationApplied System Innovation-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial intelligence-
Palavras-chave: dc.subjectcontinuation power flow-
Palavras-chave: dc.subjectcritical point-
Palavras-chave: dc.subjectprediction-
Título: dc.titleEstimation of Total Real and Reactive Power Losses in Electrical Power Systems via Artificial Neural Network-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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