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State Estimation Fusion for Linear Microgrids over an Unreliable Network

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorSoleymannejad, Mohammad
dc.contributor.authorSadrian Zadeh, Danial
dc.contributor.authorMoshiri, Behzad
dc.contributor.authorSadjadi, Ebrahim
dc.contributor.authorGarcía Herrero, Jesús
dc.contributor.authorMolina López, José Manuel
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es
dc.contributor.funderUniversidad Carlos III de Madrides
dc.date.accessioned2022-06-21T10:25:20Z
dc.date.available2022-06-21T10:25:20Z
dc.date.issued2022-03-21
dc.description.abstractMicrogrids should be continuously monitored in order to maintain suitable voltages over time. Microgrids are mainly monitored remotely, and their measurement data transmitted through lossy communication networks are vulnerable to cyberattacks and packet loss. The current study leverages the idea of data fusion to address this problem. Hence, this paper investigates the effects of estimation fusion using various machine-learning (ML) regression methods as data fusion methods by aggregating the distributed Kalman filter (KF)-based state estimates of a linear smart microgrid in order to achieve more accurate and reliable state estimates. This unreliability in measurements is because they are received through a lossy communication network that incorporates packet loss and cyberattacks. In addition to ML regression methods, multi-layer perceptron (MLP) and dependent ordered weighted averaging (DOWA) operators are also employed for further comparisons. The results of simulation on the IEEE 4-bus model validate the effectiveness of the employed ML regression methods through the RMSE, MAE and R-squared indices under the condition of missing and manipulated measurements. In general, the results obtained by the Random Forest regression method were more accurate than those of other methods.en
dc.description.sponsorshipThis research was partially funded by public research projects of Spanish Ministry of Science and Innovation, references PID2020-118249RB-C22 and PDC2021-121567-C22 - AEI/10.13039/ 501100011033, and by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors, reference EPUC3M17.en
dc.format.extent24
dc.identifier.bibliographicCitationSoleymannejad, M., Sadrian Zadeh, D., Moshiri, B., Sadjadi, E. N., Herrero, J. G., & López, J. M. M. (2022). State Estimation Fusion for Linear Microgrids over an Unreliable Network. In Energies, 15(6), 2288-2312en
dc.identifier.doihttps://doi.org/10.3390/en15062288
dc.identifier.issn1996-1073
dc.identifier.publicationfirstpage2288
dc.identifier.publicationissue6
dc.identifier.publicationlastpage2312
dc.identifier.publicationtitleEnergies (Energies)en
dc.identifier.publicationvolume15
dc.identifier.urihttps://hdl.handle.net/10016/35203
dc.identifier.uxxiAR/0000030913
dc.language.isoengen
dc.publisherMDPI AGen
dc.relation.projectIDGobierno de España. PID2020-118249RB-C22es
dc.relation.projectIDGobierno de España. PDC2021-121567-C22 - AEI/10.13039/ 501100011033es
dc.relation.projectIDUniversidad Carlos III de Madrid. EPUC3M17es
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.subject.otherCyberattacken
dc.subject.otherData fusionen
dc.subject.otherEstimation fusionen
dc.subject.otherInternet of thingsen
dc.subject.otherKalman filteren
dc.subject.otherMachineen
dc.subject.otherLearningen
dc.subject.otherPacket lossen
dc.subject.otherSmart microgriden
dc.subject.otherState estimationen
dc.titleState Estimation Fusion for Linear Microgrids over an Unreliable Networken
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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