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dc.contributor.author | Elvira-Ortiz, David A. | es_ES |
dc.contributor.author | Saucedo-Dorantes, Juan J. | es_ES |
dc.contributor.author | Osornio-Rios, Roque A. | es_ES |
dc.contributor.author | Morinigo-Sotelo, Daniel | es_ES |
dc.contributor.author | Antonino-Daviu, J. | es_ES |
dc.date.accessioned | 2023-03-30T18:01:11Z | |
dc.date.available | 2023-03-30T18:01:11Z | |
dc.date.issued | 2022-01 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/192656 | |
dc.description.abstract | [EN] Wind generation is an essential power supply in the last time, as a renewable option. These wind generators are integrated with electrical machines that require correct functionality. However, the increasing use of non-linear loads introduces undesired disturbances that may compromise the integrity of the electrical machines inside the wind generator. Therefore, this work proposes a 5-step methodology for power quality disturbance detection in grids with injection of wind farm energy. First, a database with synthetic signals is generated to be used in the training process. Then, a multi-domain feature estimation is carried out. To reduce the problem dimensionality, the features that provide redundant information are eliminated through an optimized feature selec-tion performed by means of a genetic algorithm and the principal component analysis. Addi-tionally, each one of the characteristic feature matrices of every considered condition is modeled through a specific self-organizing map neuron grid so they can be shown in a 2-D representation. Since the SOM model provides a pattern of the behavior of every disturbance, they are used as inputs of the classifier based in a softmax layer neural network that performs the power quality disturbance detection of six different conditions: healthy or normal, sag or swell voltages, tran-sients, voltage fluctuations and harmonic distortion. Thus, the proposed method is validated using a set of synthetic signals and then it is tested using two different sets of real signals from an IEEE workgroup and from a wind park located in Spain. | es_ES |
dc.description.sponsorship | This research was partially funded by FONDEC-UAQ 2020 FIN202011 project. It was also supported by the Spanish `Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the `Proyectos de I+D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Electronics | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Artificial intelligence | es_ES |
dc.subject | Electrical machines | es_ES |
dc.subject | Optimization techniques | es_ES |
dc.subject | Self-organizing map | es_ES |
dc.subject | Power quality | es_ES |
dc.subject | Wind generation | es_ES |
dc.subject.classification | INGENIERIA ELECTRICA | es_ES |
dc.title | Power Quality Monitoring Strategy Based on an Optimized Multi-domain Feature Selection for the Detection and Classification of Disturbances in Wind Generators | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/electronics11020287 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-095747-B-I00/ES/TECNOLOGIAS AVANZADAS BASADAS EN EL ANALISIS DEL FLUJO DE DISPERSION EN REGIMEN TRANSITORIO PARA EL DIAGNOSTICO PRECOZ DE ANOMALIAS ELECTROMECANICAS EN MOTORES ELECTRICOS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/FONDECYT//FIN202011/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials | es_ES |
dc.description.bibliographicCitation | Elvira-Ortiz, DA.; Saucedo-Dorantes, JJ.; Osornio-Rios, RA.; Morinigo-Sotelo, D.; Antonino-Daviu, J. (2022). Power Quality Monitoring Strategy Based on an Optimized Multi-domain Feature Selection for the Detection and Classification of Disturbances in Wind Generators. Electronics. 11(2):1-25. https://doi.org/10.3390/electronics11020287 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/electronics11020287 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 25 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 11 | es_ES |
dc.description.issue | 2 | es_ES |
dc.identifier.eissn | 2079-9292 | es_ES |
dc.relation.pasarela | S\453220 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica, Perú | es_ES |