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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorCortes-Robles, Oswaldo-
dc.contributor.authorBarocio, Emilio-
dc.contributor.authorObusevs, Artjoms-
dc.contributor.authorKorba, Petr-
dc.contributor.authorSegundo Sevilla, Felix Rafael-
dc.date.accessioned2021-12-20T09:49:19Z-
dc.date.available2021-12-20T09:49:19Z-
dc.date.issued2021-
dc.identifier.issn0263-2241de_CH
dc.identifier.issn1873-412Xde_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/23777-
dc.description.abstractIn this paper, a deep learning approach for power quality monitoring in systems with distributed generation sources is presented. The proposed method focuses in the multi-scale analysis of multi-component signals for power quality disturbances classification. The proposed methodology combines a signal processing stage using variational mode decomposition (VMD) to obtain the times scales of multi-component signals, and a deep learning stage using a simple feedforward neural network (FFNN) to classify the disturbances. The simple proposed architecture allows minimum training time of the classification model. In addition, the proposed method is able to classify different disturbance combinations based on a reduced training-set. The proposed VMD-FFNN method is tested using synthetic and simulated signals, and it is compared with other well-known methods based on convolutional and recurrent deep neuronal networks. Finally, the proposed method is assessed using lab measurements in order to shown its performance in a real-world environment.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofMeasurementde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectPower qualityde_CH
dc.subjectDisturbance classificationde_CH
dc.subjectVariational mode decompositionde_CH
dc.subjectDeep learningde_CH
dc.subjectDistributed generation sourcesde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titleFast-training feedforward neural network for multi-scale power quality monitoring in power systems with distributed generation sourcesde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Energiesysteme und Fluid-Engineering (IEFE)de_CH
dc.identifier.doi10.1016/j.measurement.2020.108690de_CH
zhaw.funding.euNode_CH
zhaw.issue108690de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume170de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Cortes-Robles, O., Barocio, E., Obusevs, A., Korba, P., & Segundo Sevilla, F. R. (2021). Fast-training feedforward neural network for multi-scale power quality monitoring in power systems with distributed generation sources. Measurement, 170(108690). https://doi.org/10.1016/j.measurement.2020.108690
Cortes-Robles, O. et al. (2021) ‘Fast-training feedforward neural network for multi-scale power quality monitoring in power systems with distributed generation sources’, Measurement, 170(108690). Available at: https://doi.org/10.1016/j.measurement.2020.108690.
O. Cortes-Robles, E. Barocio, A. Obusevs, P. Korba, and F. R. Segundo Sevilla, “Fast-training feedforward neural network for multi-scale power quality monitoring in power systems with distributed generation sources,” Measurement, vol. 170, no. 108690, 2021, doi: 10.1016/j.measurement.2020.108690.
CORTES-ROBLES, Oswaldo, Emilio BAROCIO, Artjoms OBUSEVS, Petr KORBA und Felix Rafael SEGUNDO SEVILLA, 2021. Fast-training feedforward neural network for multi-scale power quality monitoring in power systems with distributed generation sources. Measurement. 2021. Bd. 170, Nr. 108690. DOI 10.1016/j.measurement.2020.108690
Cortes-Robles, Oswaldo, Emilio Barocio, Artjoms Obusevs, Petr Korba, and Felix Rafael Segundo Sevilla. 2021. “Fast-Training Feedforward Neural Network for Multi-Scale Power Quality Monitoring in Power Systems with Distributed Generation Sources.” Measurement 170 (108690). https://doi.org/10.1016/j.measurement.2020.108690.
Cortes-Robles, Oswaldo, et al. “Fast-Training Feedforward Neural Network for Multi-Scale Power Quality Monitoring in Power Systems with Distributed Generation Sources.” Measurement, vol. 170, no. 108690, 2021, https://doi.org/10.1016/j.measurement.2020.108690.


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