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dc.contributor.authorRodríguez-Rivero, Jacob
dc.contributor.authorRamírez, Javier
dc.contributor.authorMartínez-Murcia, Francisco Jesús
dc.contributor.authorSegovia, Fermín
dc.contributor.authorOrtiz-García, Andrés 
dc.contributor.authorSalas-González, Diego
dc.contributor.authorCastillo-Barnes, Diego
dc.contributor.authorÁlvarez-Illán, Ignacio
dc.contributor.authorPuntonet, Carlos
dc.contributor.authorJiménez-Mesa, Carmen
dc.contributor.authorLeiva, Francisco J.
dc.contributor.authorCarrillo, Susana
dc.contributor.authorSuckling, John
dc.contributor.authorGórriz-Sáez, Juan Manuel
dc.date.accessioned2023-11-22T11:47:56Z
dc.date.available2023-11-22T11:47:56Z
dc.date.issued2019-05-01
dc.identifier.citationRodriguez-Rivero, J. & Ramírez, Javier & Martínez-Murcia, F.J. & Segovia, F. & Ortiz, Andrés & Salas, D. & Castillo, D. & Illan, Ignacio & Puntonet, Carlos & Jiménez Mesa, Carmen & Leiva, F.J. & Carillo, S. & Suckling, John & Gorriz, Juan. (2019). Granger Causality-based Information Fusion Applied to Electrical Measurements from Power Transformers. Information Fusion. 57. 10.1016/j.inffus.2019.12.005.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/28107
dc.description.abstractIn the immediate future, with the increasing presence of electrical vehicles and the large increase in the use of renewable energies, it will be crucial that distribution power networks are managed, supervised and exploited in a similar way as the transmission power systems were in previous decades. To achieve this, the underlying infrastructure requires automated monitoring and digitization, including smart-meters, wide-band communication systems, electronic device based-local controllers, and the Internet of Things. All of these technologies demand a huge amount of data to be curated, processed, interpreted and fused with the aim of real-time predictive control and supervision of medium/low voltage transformer substations. Wiener–Granger causality, a statistical notion of causal inference based on Information Fusion could help in the prediction of electrical behaviour arising from common causal dependencies. Originally developed in econometrics, it has successfully been applied to several fields of research such as the neurosciences and is applicable to time series data whereby cause precedes effect. In this paper, we demonstrate the potential of this methodology in the context of power measures for providing theoretical models of low/medium power transformers. Up to our knowledge, the proposed method in this context is the first attempt to build a data-driven power system model based on G-causality. In particular, we analysed directed functional connectivity of electrical measures providing a statistical description of observed responses, and identified the causal structure within data in an exploratory analysis. Pair-wise conditional G-causality of power transformers, their independent evolution in time, and the joint evolution in time and frequency are discussed and analysed in the experimental section.es_ES
dc.description.sponsorshipThis work was partly supported by the MINECO/ FEDER under the RTI2018- 098913-B100 project. The authors would like to acknowledge the support of 370 CDTI (Centro para el Desarrollo Tecnologico Industrial, Ministerio de Cien cia, Innovacion y Universidades and FEDER, SPAIN) under the PASTORA project (Ref.: ITC-20181102). and to thank the companies within the PAS TORA consortium: Endesa, Ayesa, Ormaz´abal and Ingelectus. We would like to thank the reviewers for their thoughtful comments and efforts towards im 375 proving our manuscript. Finally, JM Gorriz would like to thank Dr G´omez Exp´osito for his helpful advice and comments.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAnálisis de series temporaleses_ES
dc.subjectSistemas electroenergéticoses_ES
dc.subject.otherGranger causalityes_ES
dc.subject.otherPower transformerses_ES
dc.subject.otherFunctional connectivityes_ES
dc.subject.otherSCADA measurementses_ES
dc.subject.otherTime series analysises_ES
dc.titleGranger Causality-based Information Fusion Applied to Electrical Measurements from Power Transformers.es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.identifier.doi10.1016/j.inffus.2019.12.005
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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