Artificial Neural Networks: Multilayer Perceptron and Radial Basis to Obtain Post-Contingency Loading Margin in Electrical Power Systems

Página do item simplificado
dc.contributor.authorBonini Neto, Alfredo [UNESP]
dc.contributor.authorAlves, Dilson Amancio [UNESP]
dc.contributor.authorMinussi, Carlos Roberto [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-07-29T15:14:25Z
dc.date.available2023-07-29T15:14:25Z
dc.date.issued2022-11-01
dc.description.abstractThis paper presents the ANN (Artificial Neural Networks) approach to obtaining complete P-V curves of electrical power systems subjected to contingency. Two networks were presented: the MLP (multilayer perceptron) and the RBF (radial basis function) networks. The differential of our methodology consisted in the speed of obtaining all the P-V curves of the system. The great advantage of using ANN models is that they can capture the nonlinear characteristics of the studied system to avoid iterative procedures. The applicability and effectiveness of the proposed methodology have been investigated on IEEE test systems (14 buses) and compared with the continuation power flow, which obtains the post-contingency loading margin starting from the base case solution. From the results, the ANN performed well, with a mean squared error (MSE) in training below the specified value. The network was able to estimate 98.4% of the voltage magnitude values within the established range, with residues around 10−4 and a percentage of success between the desired and obtained output of approximately 98%, with better result for the RBF (radial basis function) network compared to MLP (multilayer perceptron).en
dc.description.affiliationSchool of Sciences and Engineering São Paulo State University (Unesp)
dc.description.affiliationSchool of Engineering São Paulo State University (Unesp)
dc.description.affiliationUnespSchool of Sciences and Engineering São Paulo State University (Unesp)
dc.description.affiliationUnespSchool of Engineering São Paulo State University (Unesp)
dc.identifierhttp://dx.doi.org/10.3390/en15217939
dc.identifier.citationEnergies, v. 15, n. 21, 2022.
dc.identifier.doi10.3390/en15217939
dc.identifier.issn1996-1073
dc.identifier.scopus2-s2.0-85141877014
dc.identifier.urihttp://hdl.handle.net/11449/249377
dc.language.isoeng
dc.relation.ispartofEnergies
dc.sourceScopus
dc.subjectartificial intelligence
dc.subjectcontingency analysis
dc.subjectcontinuation methods
dc.subjectload flow
dc.subjectmaximum loading point
dc.subjectvoltage collapse
dc.subjectvoltage stability margin
dc.titleArtificial Neural Networks: Multilayer Perceptron and Radial Basis to Obtain Post-Contingency Loading Margin in Electrical Power Systemsen
dc.typeArtigo
unesp.author.orcid0000-0002-0250-489X[1]
unesp.author.orcid0000-0001-7540-6572[3]
unesp.departmentEngenharia Elétrica - FEISpt

Arquivos

Coleções

Ilha Solteira - FEIS - Faculdade de Engenharia